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1703.03664 | 24 | Table 2. Robot videos neg. log-likelihood in nats per sub-pixel. âTrâ is the training set, âTs-seenâ is the test set with novel arm and camera conï¬guration and previously seen objects, and âTs- novelâ is the same as âTs-seenâ but with novel objects.
Model O(N) PixelCNN O(log N) PixelCNN O(log N) PixelCNN, in-graph O(T N) VPN O(T ) VPN O(T ) VPN, in-graph O(T log N) VPN O(T log N) VPN, in-graph scale 32 32 32 64 64 64 64 64 time 120.0 1.17 1.14 1929.8 0.38 0.37 3.82 3.07 speedup 1.0Ã 102Ã 105Ã 1.0Ã 5078Ã 5215Ã 505Ã 628Ã
trained four upscaling networks to produce up to 128 Ã 128 samples.At scales 64 Ã 64 and above, during training we randomly cropped the image to 32 Ã 32. This accelerates training but does not pose a problem at test time because all of the networks are fully convolutional. | 1703.03664#24 | Parallel Multiscale Autoregressive Density Estimation | PixelCNN achieves state-of-the-art results in density estimation for natural
images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
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1703.03664 | 25 | Table 4. Sampling speed of several models in seconds per frame on an Nvidia Quadro M4000 GPU. The top three rows were mea- sured on 32Ã32 ImageNet, with batch size of 30. The bottom ï¬ve rows were measured on generating 64 à 64 videos of 18 frames each, averaged over 5 videos.
Table 3 shows the results. On both 32 à 32 and 64 à 64 ImageNet it achieves signiï¬cantly better likelihood scores than have been reported for any non-pixel-autoregressive density models, such as ConvDRAW and Real NVP, that also allow eï¬cient sampling.
ing from 8 Ã 8, but less realistic results due to the more challenging nature of the problem. Upscaling starting from 32 Ã 32 results in much more realistic images. Here the diversity is apparent in the samples (as in the data, condi- tioned on low-resolution) in the local details such as the dogâs fur patterns or the frogâs eye contours.
Of course, performance of these approaches varies consid- erably depending on the implementation details, especially in the design and capacity of deep neural networks used. But it is notable that the very simple and direct approach developed here can surpass the state-of-the-art among fast- sampling density models.
# 4.5. Sampling time comparison | 1703.03664#25 | Parallel Multiscale Autoregressive Density Estimation | PixelCNN achieves state-of-the-art results in density estimation for natural
images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
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1703.03664 | 26 | # 4.5. Sampling time comparison
As expected, we observe a very large speedup of our model compared to sampling from a standard PixelCNN at the same resolution (see Table 4). Even at 32 Ã 32 we ob- serve two orders of magnitude speedup, and the speedup is greater for higher resolution.
32 Model 3.86 (3.83) PixelRNN 3.83 (3.77) PixelCNN Real NVP 4.28(4.26) Conv. DRAW 4.40(4.35) 3.95(3.92) Ours 64 3.64(3.57) 3.57(3.48) 3.98(3.75) 4.10(4.04) 3.70(3.67) 128 - - - - 3.55(3.42)
Since our model only requires O(log N) network evalua- tions to sample, we can ï¬t the entire computation graph for sampling into memory, for reasonable batch sizes. In- graph computation in TensorFlow can further improve the speed of both image and video generation, due to reduced overhead by avoiding repeated calls to sess.run.
Table 3. ImageNet negative log-likelihood in bits per sub-pixel at 32 Ã 32, 64 Ã 64 and 128 Ã 128 resolution.
In Figure 8 we show examples of diverse 128 Ã 128 class conditional image generation. | 1703.03664#26 | Parallel Multiscale Autoregressive Density Estimation | PixelCNN achieves state-of-the-art results in density estimation for natural
images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
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|
1703.03664 | 27 | In Figure 8 we show examples of diverse 128 Ã 128 class conditional image generation.
Since our model has a PixelCNN at the lowest resolution, it can also be accelerated by caching PixelCNN hidden unit activations, recently implemented b by Ramachandran et al. (2017). This could allow one to use higher-resolution base PixelCNNs without sacriï¬cing speed.
Interestingly, the model often produced quite realistic bird images from scratch when trained on CUB, and these sam- ples looked more realistic than any animal image generated by our ImageNet models. One plausible explanation for this diï¬erence is a lack of model capacity; a single network modeling the 1000 very diverse ImageNet categories can devote only very limited capacity to each one, compared to a network that only needs to model birds. This sug- gests that ï¬nding ways to increase capacity without slowing down training or sampling could be a promising direction.
# 5. Conclusions | 1703.03664#27 | Parallel Multiscale Autoregressive Density Estimation | PixelCNN achieves state-of-the-art results in density estimation for natural
images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
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|
1703.03664 | 28 | # 5. Conclusions
In this paper, we developed a parallelized, multiscale ver- sion of PixelCNN. It achieves competitive density estima- tion results on CUB, MPII, MS-COCO, ImageNet, and Robot Pushing videos, surpassing all other density models that admit fast sampling. Qualitatively, it can achieve com- pelling results in text-to-image synthesis and video gener- ation, as well as diverse super-resolution from very small images all the way to 512 Ã 512.
Figure 7 shows upscaling starting from ground-truth im- ages of size 8Ã8, 16Ã16 and 32Ã32. We observe the largest diversity of samples in terms of global structure when startMany more samples from all of our models can be found in the appendix and supplementary material.
Parallel Multiscale Autoregressive Density Estimation
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images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
{
"id": "1701.05517"
},
{
"id": "1612.00005"
},
{
"id": "1612.03242"
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"id": "1610.00527"
},
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},
{
"id": "1702.00783"
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images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
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}
]
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images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
{
"id": "1701.05517"
},
{
"id": "1612.00005"
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"id": "1610.00527"
},
{
"id": "1610.10099"
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"id": "1702.00783"
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images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
{
"id": "1701.05517"
},
{
"id": "1612.00005"
},
{
"id": "1612.03242"
},
{
"id": "1610.00527"
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{
"id": "1610.10099"
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images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
{
"id": "1701.05517"
},
{
"id": "1612.00005"
},
{
"id": "1612.03242"
},
{
"id": "1610.00527"
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{
"id": "1610.10099"
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"id": "1702.00783"
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|
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Parallel Multiscale Autoregressive Density Estimation | 1703.03664#33 | Parallel Multiscale Autoregressive Density Estimation | PixelCNN achieves state-of-the-art results in density estimation for natural
images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
{
"id": "1701.05517"
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"id": "1612.00005"
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"id": "1612.03242"
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1703.03664 | 34 | Parallel Multiscale Autoregressive Density Estimation
Sønderby, Casper Kaae, Caballero, Jose, Theis, Lucas, Shi, Wenzhe, and Husz´ar, Ferenc. Amortised MAP inference for image super-resolution. 2017.
# 6. Appendix
Below we show additional samples.
Theis, L. and Bethge, M. Generative image modeling using spatial LSTMs. In NIPS, 2015.
Iain, and Larochelle, Hugo. RNADE: The real-valued neural autoregressive density- estimator. In NIPS, 2013.
and Kavukcuoglu, Koray. Pixel recurrent neural networks. In ICML, pp. 1747â1756, 2016a.
van den Oord, A¨aron, Kalchbrenner, Nal, Vinyals, Oriol, Espeholt, Lasse, Graves, Alex, and Kavukcuoglu, Koray. Conditional image generation with PixelCNN decoders. In NIPS, 2016b.
Wah, Catherine, Branson, Steve, Welinder, Peter, Perona, Pietro, and Belongie, Serge. The Caltech-UCSD birds- 200-2011 dataset. 2011. | 1703.03664#34 | Parallel Multiscale Autoregressive Density Estimation | PixelCNN achieves state-of-the-art results in density estimation for natural
images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
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|
1703.03664 | 35 | Wang, Xiaolong and Gupta, Abhinav. Generative image modeling using style and structure adversarial networks. In ECCV, pp. 318â335, 2016.
Wu, Yuhuai, Burda, Yuri, Salakhutdinov, Ruslan, and Grosse, Roger. On the quantitative analysis of decoder- based generative models. 2017.
Zhang, Han, Xu, Tao, Li, Hongsheng, Zhang, Shaoting, Huang, Xiaolei, Wang, Xiaogang, and Metaxas, Dim- itris. StackGAN: Text to photo-realistic image synthe- sis with stacked generative adversarial networks. arXiv preprint arXiv:1612.03242, 2016.
Parallel Multiscale Autoregressive Density Estimation | 1703.03664#35 | Parallel Multiscale Autoregressive Density Estimation | PixelCNN achieves state-of-the-art results in density estimation for natural
images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
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1703.03664 | 36 | oe ae ee Be ite striped breast, 5 â < A bird with a short neck, yellow eyebrows and brown and whi neck and primaries. sail oak fam ak soi beak ail A yellow bird with a black head, orange eyes and an orange bill. ak. sail Beak tail 503 This little bird has a thin long curved down beak white under-body and brow head wings back and tail. Awhite large bird with orange legs and gray secondaries and primaries, and a short yellow bill. beak rail eak au 3k sail The bird has a small bill that is black and a white breast. pu peak gout beak oi beak rau beak i Ld a White bellied bird has black and orange breast, black head and straight black tail. tai beak sail beak tit beak toi beak The bird is round with a green crown and white belly. ake ai beak you eae âSmall light brown bird with black rectricles and a long white beak. ti beak rail eae soi beak tit beak The small brown bird has an ivory belly with dark brown stripes on its crown. This bird has a white belly and breast with a black back and red crown and nape. beak beak _ beak An aquatic bird with a | 1703.03664#36 | Parallel Multiscale Autoregressive Density Estimation | PixelCNN achieves state-of-the-art results in density estimation for natural
images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
{
"id": "1701.05517"
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"id": "1612.00005"
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"id": "1612.03242"
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|
1703.03664 | 37 | on its crown. This bird has a white belly and breast with a black back and red crown and nape. beak beak _ beak An aquatic bird with a long, two toned neck with red eyes. rai weak sail beak fm 3k oi beak i This is a large brown bird with a bright green head, yellow bill and orange feet. pau beak sail beak ait beak toi beak 4 This magnificent specimen has a white belly, pink breast and neck, with black superciliary and white winabars. no Es A bird with a red bill that has a pointed black tip, white wing bars, a small head, white throat and belly. beak it beak st 3 a =| = This bird has a white back , breast and belly with a black crown and long The bird has curved feet that are black and a small bill. oak beak soi With long brown upper converts and giant white wings, the grey breasted bird flies through the air. | 1703.03664#37 | Parallel Multiscale Autoregressive Density Estimation | PixelCNN achieves state-of-the-art results in density estimation for natural
images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
{
"id": "1701.05517"
},
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"id": "1612.00005"
},
{
"id": "1612.03242"
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|
1703.03664 | 38 | Figure 9. Additional CUB samples randomly chosen from the validation set.
Parallel Multiscale Autoregressive Density Estimation
A blurry photo of a woman swimming underwater in a pool pelvis head gelvis head elvis ead Aman ina black shirt and blue jeans is washing a black car. Aman wearing camo is fixing a large gun on the table. head elvis head ee a head ead head An elderly man in a black striped shirt holding a yellow handle. eag has ead ead |_| Aman in a white shirt with a black vest is driving a boat. ie elvis head pelvis head vis A middle-aged man is wearing a bicycling outfit and a red helmet and has the number 96 on his handlebars. Aman in a white and green shirt is standing next to a tiller. head head ead Aman ina tight fitting red outfit is doing a gymnastics move. a man ina blue shirt and pants is doing a pull up on metal bar at wooden poles. head elvis head vis head This man i holding a large package and wheeling it down a hallway. Aman in a red shirt and blue overalls who is chopping wood with a large ax.
Figure 10. Additional MPII samples randomly chosen from the validation set.
Parallel Multiscale Autoregressive Density Estimation | 1703.03664#38 | Parallel Multiscale Autoregressive Density Estimation | PixelCNN achieves state-of-the-art results in density estimation for natural
images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
{
"id": "1701.05517"
},
{
"id": "1612.00005"
},
{
"id": "1612.03242"
},
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"id": "1610.00527"
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"id": "1610.10099"
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"id": "1702.00783"
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"id": "1609.03499"
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|
1703.03664 | 39 | Figure 10. Additional MPII samples randomly chosen from the validation set.
Parallel Multiscale Autoregressive Density Estimation
person 05 ed | Three people on the beach with one holding a surfboard rT - ry | = Sopanaeeret it â See ee e âTwo horses are in the grass by the woods. arte, = A set of four buses parked next to each other ona parking lot. A bus is being towed by a blue tow truck A building with a clock mounted on it. A train is standing at a railway station and a car is A giraffe walking through a grassy area near some parked in front of it. âwoman holding a baby while sitting in front of a cake layer bunting a baseball at a game. airplane oka Alarge white airplane parked in a stationary position. A bunch of trucks parked next to each other. Three horses and a foal in an enclosed field. person airplane f a \ eel j é a nif I nif Mi ed Sat âSome white sheep are in a brown pen. âA young skier is looking away while people in the large commercial airplane taking off from the landing background look on. stripe. A black roman numeral clock on a building. Aman sitting at a desk covered with papers. A smart phone sitting next to a receipt on a table
Figure 11. Additional MS-COCO samples randomly chosen from the validation set. | 1703.03664#39 | Parallel Multiscale Autoregressive Density Estimation | PixelCNN achieves state-of-the-art results in density estimation for natural
images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
{
"id": "1701.05517"
},
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"id": "1612.00005"
},
{
"id": "1612.03242"
},
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"id": "1610.00527"
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"id": "1610.10099"
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"id": "1702.00783"
},
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|
1703.03664 | 40 | Figure 11. Additional MS-COCO samples randomly chosen from the validation set.
Parallel Multiscale Autoregressive Density Estimation
N iN) = = 2 2 = = o ae 88 BP Be EP SF ro pm BP BF SE SP os Py P95 OM KS5 XH Qo FO ©5 OH KS XH x xo x xe &® oe 25 209 x xe ® oe Ny N = aS & B x xe 4 4 g z 3 8 BB Ss & BB o © Fy Q © x 3 â ¢ ioe ao if a âiz lon cas <i 4 17 a 4 L er | 7 ES te 4 fo baal aoe "N ⢠4 4 as a & = eo as ear a, a 1 ee ee es _â* Se Se ee re Se cod nel ne al ae re fe] a 4 4 2 poe poe poe a a ae rane Fae iw at ae ey ae a Ae tee Cw Ew Cw | a ap be Log ai (> <a Av | ee gle ral sel ral sa bea lis Ts boar boos bene | = Tei enon aa ie } fe 7 } fe Hot red eb Eas ioe ea | | 1703.03664#40 | Parallel Multiscale Autoregressive Density Estimation | PixelCNN achieves state-of-the-art results in density estimation for natural
images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling. | http://arxiv.org/pdf/1703.03664 | Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas | cs.CV, cs.NE | null | null | cs.CV | 20170310 | 20170310 | [
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|
1703.03400 | 0 | 7 1 0 2
l u J 8 1 ] G L . s c [
3 v 0 0 4 3 0 . 3 0 7 1 : v i X r a
# Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
# Chelsea Finn 1 Pieter Abbeel 1 2 Sergey Levine 1
# Abstract
the form of computation required to complete the task.
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is com- patible with any model trained with gradient de- scent and applicable to a variety of different learning problems, including classiï¬cation, re- gression, and reinforcement learning. The goal of meta-learning is to train a model on a vari- ety of learning tasks, such that it can solve new learning tasks using only a small number of train- ing samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to ï¬ne-tune. We demonstrate that this approach leads to state-of-the-art performance on two few- shot image classiï¬cation benchmarks, produces good results on few-shot regression, and acceler- ates ï¬ne-tuning for policy gradient reinforcement learning with neural network policies. | 1703.03400#0 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
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"id": "1508.03854"
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}
]
|
1703.03429 | 0 | 7 1 0 2
r a M 9 ] I A . s c [
1 v 9 2 4 3 0 . 3 0 7 1 : v i X r a
# What can you do with a rock? Affordance extraction via word embeddings
Nancy Fulda and Daniel Ricks and Ben Murdoch and David Wingate {nfulda, daniel ricks, murdoch, wingated}@byu.edu
# Brigham Young University
# Abstract
mense wealth of common sense knowledge implicitly en- coded in online corpora.
Autonomous agents must often detect affordances: the set of behaviors enabled by a situation. Af- fordance detection is particularly helpful in do- mains with large action spaces, allowing the agent to prune its search space by avoiding futile be- haviors. This paper presents a method for affor- dance extraction via word embeddings trained on a Wikipedia corpus. The resulting word vectors are treated as a common knowledge database which can be queried using linear algebra. We apply this method to a reinforcement learning agent in a text-only environment and show that affordance- based action selection improves performance most of the time. Our method increases the computa- tional complexity of each learning step but signif- icantly reduces the total number of steps needed. In addition, the agentâs action selections begin to resemble those a human would choose.
# 1 Introduction | 1703.03429#0 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 1 | # 1. Introduction
Learning quickly is a hallmark of human intelligence, whether it involves recognizing objects from a few exam- ples or quickly learning new skills after just minutes of experience. Our artiï¬cial agents should be able to do the same, learning and adapting quickly from only a few exam- ples, and continuing to adapt as more data becomes avail- able. This kind of fast and ï¬exible learning is challenging, since the agent must integrate its prior experience with a small amount of new information, while avoiding overï¬t- ting to the new data. Furthermore, the form of prior ex- perience and new data will depend on the task. As such, for the greatest applicability, the mechanism for learning to learn (or meta-learning) should be general to the task and
1University of California, Berkeley 2OpenAI. Correspondence to: Chelsea Finn <cbï¬[email protected]>.
Proceedings of the 34 th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017. Copyright 2017 by the author(s). | 1703.03400#1 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
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},
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"id": "1603.04467"
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]
|
1703.03429 | 1 | # 1 Introduction
The physical world is ï¬lled with constraints. You can open a door, but only if it isnât locked. You can douse a ï¬re, but only if a ï¬re is present. You can throw a rock or drop a rock or even, under certain circumstances, converse with a rock, but you cannot traverse it, enumerate it, or impeach it. The term affordances [Gibson, 1977] refers to the subset of possible actions which are feasible in a given situation. Human beings detect these affordances automatically, often subconsciously, but it is not uncommon for autonomous learning agents to attempt impossible or even ridiculous actions, thus wasting effort on futile behaviors. | 1703.03429#1 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 2 | In this work, we propose a meta-learning algorithm that is general and model-agnostic, in the sense that it can be directly applied to any learning problem and model that is trained with a gradient descent procedure. Our focus is on deep neural network models, but we illustrate how our approach can easily handle different architectures and different problem settings, including classiï¬cation, regres- sion, and policy gradient reinforcement learning, with min- imal modiï¬cation. In meta-learning, the goal of the trained model is to quickly learn a new task from a small amount of new data, and the model is trained by the meta-learner to be able to learn on a large number of different tasks. The key idea underlying our method is to train the modelâs initial parameters such that the model has maximal perfor- mance on a new task after the parameters have been up- dated through one or more gradient steps computed with a small amount of data from that new task. Unlike prior meta-learning methods that learn an update function or learning rule (Schmidhuber, 1987; Bengio et al., 1992; Andrychowicz et al., 2016; Ravi & Larochelle, 2017), our algorithm does not expand the number of learned param- eters nor place | 1703.03400#2 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
"id": "1612.00796"
},
{
"id": "1611.02779"
},
{
"id": "1603.04467"
},
{
"id": "1703.05175"
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"id": "1508.03854"
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"id": "1611.05763"
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]
|
1703.03429 | 2 | This paper presents a method for affordance extraction based on the copiously available linguistic information in on- line corpora. Word embeddings trained using Wikipedia arti- cles are treated as a common sense knowledge base that en- codes (among other things) object-speciï¬c affordances. Be- cause knowledge is represented as vectors, the knowledge base can be queried using linear algebra. This somewhat counterintuitive notion - the idea that words can be manip- ulated mathematically - creates a theoretical bridge between the frustrating realities of real-world systems and the imWe apply our technique to a text-based environment and show that a priori knowledge provided by affordance ex- traction greatly speeds learning. Speciï¬cally, we reduce the agentâs search space by (a) identifying actions afforded by a given object; and (b) discriminating objects that can be grasped, lifted and manipulated from objects which can merely be observed. Because the agent explores only those actions which âmake senseâ, it is able to discover valuable be- haviors more quickly than a comparable agent using a brute force approach. Critically, the affordance agent is demon- strably able to eliminate extraneous actions without (in most cases) discarding beneï¬cial ones.
# 2 Related Work | 1703.03429#2 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 3 | al., 1992; Andrychowicz et al., 2016; Ravi & Larochelle, 2017), our algorithm does not expand the number of learned param- eters nor place constraints on the model architecture (e.g. by requiring a recurrent model (Santoro et al., 2016) or a Siamese network (Koch, 2015)), and it can be readily com- bined with fully connected, convolutional, or recurrent neu- ral networks. It can also be used with a variety of loss func- tions, including differentiable supervised losses and non- differentiable reinforcement learning objectives. | 1703.03400#3 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
"id": "1612.00796"
},
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"id": "1611.02779"
},
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"id": "1603.04467"
},
{
"id": "1703.05175"
},
{
"id": "1508.03854"
},
{
"id": "1611.05763"
}
]
|
1703.03429 | 3 | # 2 Related Work
Our research relies heavily on word2vec [Mikolov et al., 2013a], an algorithm that encodes individual words based on the contexts in which they tend to appear. Earlier work has shown that word vectors trained using this method con- tain intriguing semantic properties, including structured rep- resentations of gender and geography [Mikolov et al., 2013b; Mikolov et al., 2013c]. The (by now) archetypal example of such properties is represented by the algebraic expres- sion vector[âkingâ] â vector[âmanâ] + vector[âwomanâ] = vector[âqueenâ]. | 1703.03429#3 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 4 | The process of training a modelâs parameters such that a few gradient steps, or even a single gradient step, can pro- duce good results on a new task can be viewed from a fea- ture learning standpoint as building an internal representa- tion that is broadly suitable for many tasks. If the internal representation is suitable to many tasks, simply ï¬ne-tuning the parameters slightly (e.g. by primarily modifying the top layer weights in a feedforward model) can produce good results. In effect, our procedure optimizes for models that are easy and fast to ï¬ne-tune, allowing the adaptation to happen in the right space for fast learning. From a dynami- cal systems standpoint, our learning process can be viewed as maximizing the sensitivity of the loss functions of new tasks with respect to the parameters: when the sensitivity is high, small local changes to the parameters can lead to
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
large improvements in the task loss. | 1703.03400#4 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
"id": "1612.00796"
},
{
"id": "1611.02779"
},
{
"id": "1603.04467"
},
{
"id": "1703.05175"
},
{
"id": "1508.03854"
},
{
"id": "1611.05763"
}
]
|
1703.03429 | 4 | Researchers have leveraged these properties for diverse ap- plications including sentence- and paragraph-level encoding [Kiros et al., 2015; Le and Mikolov, 2014], image catego- rization [Frome et al., 2013], bidirectional retrieval [Karpa- thy et al., 2014], semantic segmentation [Socher et al., 2011], biomedical document retrieval [Brokos et al., 2016], and the alignment of movie scripts to their corresponding source texts [Zhu et al., 2015]. Our work is most similar to [Zhu et al., 2014]; however, rather than using a Markov Logic Network to build an explicit knowledge base, we instead rely on the semantic structure implicitly encoded in skip-grams.
Affordance detection, a topic of rising importance in our increasingly technological society, has been attempted and/or accomplished using visual characteristics [Song et al., 2011; Song et al., 2015], haptic data [Navarro et al., 2012], visuo- motor simulation [Schenck et al., 2012; Schenck et al., 2016], repeated real-world experimentation [Montesano et al., 2007;
Stoytchev, 2008], and knowledge base representations [Zhu et al., 2014]. | 1703.03429#4 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 5 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
large improvements in the task loss.
The primary contribution of this work is a simple model- and task-agnostic algorithm for meta-learning that trains a modelâs parameters such that a small number of gradi- ent updates will lead to fast learning on a new task. We demonstrate the algorithm on different model types, includ- ing fully connected and convolutional networks, and in sev- eral distinct domains, including few-shot regression, image classiï¬cation, and reinforcement learning. Our evaluation shows that our meta-learning algorithm compares favor- ably to state-of-the-art one-shot learning methods designed speciï¬cally for supervised classiï¬cation, while using fewer parameters, but that it can also be readily applied to regres- sion and can accelerate reinforcement learning in the pres- ence of task variability, substantially outperforming direct pretraining as initialization.
# 2. Model-Agnostic Meta-Learning
We aim to train models that can achieve rapid adaptation, a problem setting that is often formalized as few-shot learn- ing. In this section, we will deï¬ne the problem setup and present the general form of our algorithm.
# 2.1. Meta-Learning Problem Set-Up | 1703.03400#5 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
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},
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},
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"id": "1603.04467"
},
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"id": "1703.05175"
},
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},
{
"id": "1611.05763"
}
]
|
1703.03429 | 5 | Stoytchev, 2008], and knowledge base representations [Zhu et al., 2014].
In 2001 [Laird and van Lent, 2001] identiï¬ed text-based adventure games as a step toward general problem solving. The same year at AAAI, Mark DePristo and Robert Zubek unveiled a hybrid system for text-based game play [Arkin, 1998], which operated on hand-crafted logic trees combined with a secondary sensory system used for goal selection. The handcrafted logic worked well, but goal selection broke down and became cluttered due to the scale of the environment. Perhaps most notably, in 2015 [Narasimhan et al., 2015] de- signed an agent which passed the text output of the game through an LSTM [Hochreiter and Schmidhuber, 1997] to ï¬nd a state representation, then used a DQN [Mnih et al., 2015] to select a Q-valued action. This approach appeared to work well within a small discrete environment with reliable state action pairs, but as the complexity and alphabet of the environment grew, the clarity of Q-values broke down and left them with a negative overall reward. Our work, in contrast, is able to ï¬nd meaningful state action pairs even in complex environments with many possible actions.
# 3 Wikipedia as a Common Sense Knowledge Base | 1703.03429#5 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 6 | # 2.1. Meta-Learning Problem Set-Up
The goal of few-shot meta-learning is to train a model that can quickly adapt to a new task using only a few datapoints and training iterations. To accomplish this, the model or learner is trained during a meta-learning phase on a set of tasks, such that the trained model can quickly adapt to new tasks using only a small number of examples or trials. In effect, the meta-learning problem treats entire tasks as training examples. In this section, we formalize this meta- learning problem setting in a general manner, including brief examples of different learning domains. We will dis- cuss two different learning domains in detail in Section 3. | 1703.03400#6 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
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},
{
"id": "1611.02779"
},
{
"id": "1603.04467"
},
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"id": "1703.05175"
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{
"id": "1508.03854"
},
{
"id": "1611.05763"
}
]
|
1703.03429 | 6 | # 3 Wikipedia as a Common Sense Knowledge Base
Google âknowledge baseâ, and youâll get a list of hand-crafted systems, both commercial and academic, with strict con- straints on encoding methods. These highly-structured, often node-based solutions are successful at a wide variety of tasks including topic gisting [Liu and Singh, 2004], affordance de- tection [Zhu et al., 2014] and general reasoning [Russ et al., 2011]. Traditional knowledge bases are human-interpretable, closely tied to high-level human cognitive functions, and able to encode complex relationships compactly and effectively. | 1703.03429#6 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 7 | We consider a model, denoted f , that maps observa- tions x to outputs a. During meta-learning, the model is trained to be able to adapt to a large or inï¬nite num- ber of tasks. Since we would like to apply our frame- work to a variety of learning problems, from classiï¬ca- tion to reinforcement learning, we introduce a generic notion of a learning task below. Formally, each task T = {L(x1, a1, . . . , xH , aH ), q(x1), q(xt+1|xt, at), H} consists of a loss function L, a distribution over initial ob- servations q(x1), a transition distribution q(xt+1|xt, at), and an episode length H. In i.i.d. supervised learning prob- lems, the length H = 1. The model may generate samples of length H by choosing an output at at each time t. The loss L(x1, a1, . . . , xH , aH ) â R, provides task-speciï¬c feedback, which might be in the form of a misclassiï¬cation loss or a cost function in a Markov decision process. | 1703.03400#7 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
"id": "1612.00796"
},
{
"id": "1611.02779"
},
{
"id": "1603.04467"
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{
"id": "1703.05175"
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"id": "1508.03854"
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|
1703.03429 | 7 | It may seem strange, then, to treat Wikipedia as a knowl- edge base. When compared with curated solutions like Con- ceptNet [Liu and Singh, 2004], Cyc [Matuszek et al., 2006], and WordNet [Miller, 1995], its contents are largely unstruc- tured, polluted by irrelevant data, and prone to user error. When used as a training corpus for the word2vec algorithm, however, Wikipedia becomes more tractable. The word vec- tors create a compact representation of the knowledge base and, as observed by [Bolukbasi et al., 2016a] and [Bolukbasi et al., 2016b], can even encode relationships about which a human author is not consciously cognizant. Perhaps most notably, Wikipedia and other online corpora are constantly updated in response to new developments and new human in- sight; hence, they do not require explicit maintenance. | 1703.03429#7 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 8 | θ âL1 âL3 âL2 θâ 3 θâ 1 θâ 2
Figure 1. Diagram of our model-agnostic meta-learning algo- rithm (MAML), which optimizes for a representation θ that can quickly adapt to new tasks.
In our meta-learning scenario, we consider a distribution over tasks p(T ) that we want our model to be able to adapt to. In the K-shot learning setting, the model is trained to learn a new task Ti drawn from p(T ) from only K samples drawn from qi and feedback LTi generated by Ti. During meta-training, a task Ti is sampled from p(T ), the model is trained with K samples and feedback from the corre- sponding loss LTi from Ti, and then tested on new samples from Ti. The model f is then improved by considering how the test error on new data from qi changes with respect to the parameters. In effect, the test error on sampled tasks Ti serves as the training error of the meta-learning process. At the end of meta-training, new tasks are sampled from p(T ), and meta-performance is measured by the modelâs perfor- mance after learning from K samples. Generally, tasks used for meta-testing are held out during meta-training. | 1703.03400#8 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
"id": "1612.00796"
},
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"id": "1603.04467"
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|
1703.03429 | 8 | However: in order to leverage the semantic structure im- plicitly encoded within Wikipedia, we must be able to in- terpret the resulting word vectors. Signiï¬cant semantic re- lationships are not readily apparent from the raw word vec- tors or from their PCA reduction. In order to extract useful information, the database must be queried through a math- ematical process. For example, in Figure 1 a dot product is used to project gendered terms onto the space deï¬ned by vector[âkingâ] â vector[âqueenâ] and vector[âwomanâ] â vector[âmanâ]. In such a projection, the mathematical re- lationship between the words is readily apparent. Masculine
actress 02 oman échoolil ._aranathether at Fie Ege ess or stewardess gather air oud evaiterctor @randfather 0.0 duke Prince echoolboy brother stallion foster Dy Be gouboy steward «ng dock emperor vector['woman'] - vector{'manâ] oul 02 aman =015 0.10 -0.05 0.00 005 0.10 O15 0.20 vectorf'king'] - vector['queen'] | 1703.03429#8 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03429 | 9 | Figure 1: Word vectors projected into the space deï¬ned by vector[âkingâ] â vector[âqueenâ] and vector[âwomanâ] â vector[âmanâ]. In this projection, masculine and feminine terms are linearly separable.
and feminine terms become linearly separable, making it easy to distinguish instances of each group.
These relationships can be leveraged to detect affordances, and thus reduce the agentâs search space. In its most general interpretation, the adjective affordant describes the set of ac- tions which are physically possible under given conditions. In the following subsections, however, we use it in the more restricted sense of actions which seem reasonable. For ex- ample, it is physically possible to eat a pencil, but it does not âmake senseâ to do so.
# 3.1 Verb/Noun affordances | 1703.03429#9 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 10 | In contrast to prior work, which has sought to train re- current neural networks that ingest entire datasets (San- toro et al., 2016; Duan et al., 2016b) or feature embed- dings that can be combined with nonparametric methods at test time (Vinyals et al., 2016; Koch, 2015), we propose a method that can learn the parameters of any standard model via meta-learning in such a way as to prepare that model for fast adaptation. The intuition behind this approach is that some internal representations are more transferrable than others. For example, a neural network might learn internal features that are broadly applicable to all tasks in p(T ), rather than a single individual task. How can we en- courage the emergence of such general-purpose representa- tions? We take an explicit approach to this problem: since the model will be ï¬ne-tuned using a gradient-based learn- ing rule on a new task, we will aim to learn a model in such a way that this gradient-based learning rule can make rapid progress on new tasks drawn from p(T ), without overï¬t- ting. In effect, we will aim to ï¬nd model parameters that are sensitive to changes in the task, such that small changes | 1703.03400#10 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
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|
1703.03429 | 10 | # 3.1 Verb/Noun affordances
So how do you teach an algorithm what âmakes senseâ? We address this challenge through an example-based query. First we provide a canonical set of verb/noun pairs which illus- trate the relationship we desire to extract from the knowl- edge base. Then we query the database using the analogy format presented by [Mikolov et al., 2013a]. Using their ter- minology, the analogy sing:song::[?]:[x] encodes the follow- ing question: If the affordant verb for âsongâ is âsingâ, then what is the affordant verb for [x]?
In theory, a single canonical example is sufï¬cient to per- form a query. However, experience has shown that results are better when multiple canonical values are averaged.
More formally, let W be the set of all English-language word vectors in our agentâs vocabulary. Further, let N = {ii,,...,71;},. NC W be the set of all nouns in W and let V = {t,..., 0}, VC W be the set of all verbs in W. | 1703.03429#10 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03429 | 11 | Let C = {(v1, 71), ..., (Gm, Tim) } represent a set of canon- ical verb/noun pairs used by our algorithm. We use C to de- fine an affordance vector @ = 1/m >> ,(@;â7i;), which can be thought of as the distance and direction within the embedding space which encodes affordant behavior.
In our experiments we used the following verb/noun pairs as our canonical set:
Our algorithm vanquish duel unsheath summon wield overpower cloak impale battle behead Co-occurrence Concept Net die have cut make ï¬ght kill move use destroy be kill parry strike slash look cool cut harm fence thrust injure
Figure 2: Verb associations for the noun âswordâ using three different methods: (1) Affordance detection using word vec- tors extracted from Wikipedia, as described in this section, (2) Strict co-occurrence counts using a Wikipedia corpus and a co-occurrence window of 9 words, (3) Results generated using ConceptNetâs CapableOf relationship. | 1703.03429#11 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 12 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Algorithm 1 Model-Agnostic Meta-Learning Require: p(T ): distribution over tasks Require: α, β: step size hyperparameters 1: randomly initialize θ 2: while not done do 3: 4: 5: 6:
Sample batch of tasks Ti â¼ p(T ) for all Ti do
Evaluate Vg Ll7,; (fo) with respect to K examples Compute adapted parameters with gradient de- scent: 6; = 0 â aVoLlr, (fo)
# end for Update θ â θ â βâθ
# 7: 8: 9: end while
8: Update 0 + 0 â BVo TPT) Lr, (fo)
products, which is supported by standard deep learning li- In our braries such as TensorFlow (Abadi et al., 2016). experiments, we also include a comparison to dropping this backward pass and using a ï¬rst-order approximation, which we discuss in Section 5.2. | 1703.03400#12 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
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}
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|
1703.03429 | 12 | [âsing songâ, âdrink waterâ, âread bookâ, âeat foodâ, âwear coatâ, âdrive carâ, âride horseâ, âgive giftâ, âattack enemyâ, âsay wordâ, âopen doorâ, âclimb treeâ, âheal woundâ, âcure diseaseâ, âpaint pictureâ]
We describe a verb/noun pair (wv, 71) as affordant to the ex- tent that 7 + @ = v. Therefore, a typical knowledge base query would return the n closest verbs {t-1,..., en} to the point i+ a
For example, using the canonical set listed above and a set of pre-trained word vectors, a query using 7% = vec- tor[âswordâ] returns the following:
[âvanquishâ, âduelâ, âunsheatheâ, âwieldâ, âsum- monâ, âbeheadâ, âbattleâ, âimpaleâ, âoverpowerâ, âcloakâ] | 1703.03429#12 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 13 | 3. Species of MAML In this section, we discuss speciï¬c instantiations of our meta-learning algorithm for supervised learning and rein- forcement learning. The domains differ in the form of loss function and in how data is generated by the task and pre- sented to the model, but the same basic adaptation mecha- nism can be applied in both cases.
make no assumption on the form of the model, other than to assume that it is parametrized by some parameter vector θ, and that the loss function is smooth enough in θ that we can use gradient-based learning techniques.
Formally, we consider a model represented by a parametrized function fg with parameters 6. When adapt- ing to a new task 7;, the modelâs parameters 6 become 64. In our method, the updated parameter vector / is computed using one or more gradient descent updates on task 7;. For example, when using one gradient update, 0, = 0âaVoLlr,(fo)- The step size a may be fixed as a hyperparameter or meta- learned. For simplicity of notation, we will consider one gradient update for the rest of this section, but using multi- ple gradient updates is a straightforward extension. | 1703.03400#13 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
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"id": "1703.05175"
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{
"id": "1508.03854"
},
{
"id": "1611.05763"
}
]
|
1703.03429 | 13 | Intuitively, this query process produces verbs which an- swer the question, âWhat should you do with an [x]?â. For example, when word vectors are trained on a Wikipedia cor- pus with part-of-speech tagging, the ï¬ve most affordant verbs to the noun âhorseâ are {âgallopâ, ârideâ, âraceâ, âhorseâ, âout- runâ}, and the top ï¬ve results for âkingâ are {âdethroneâ, âdis- obeyâ, âdeposeâ, âreignâ, âabdicateâ}.
The resulting lists are surprisingly logical, especially given the unstructured nature of the Wikipedia corpus from which the vector embeddings were extracted. Subjective examina- tion suggests that affordances extracted using Wikipedia are at least as relevant as those produced by more traditional methods (see Figure 2). | 1703.03429#13 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 14 | The model parameters are trained by optimizing for the per- formance of fg, with respect to across tasks sampled from p(T). More concretely, the meta-objective is as follows: min > Lr (for) = > Lr: (fo-oVo£7,(fo))
min > Lr (for) = > Lr: (fo-oVo£7,(fo)) Ti~p(T) Ti~p(T)
# 3.1. Supervised Regression and Classiï¬cation
Few-shot learning is well-studied in the domain of super- vised tasks, where the goal is to learn a new function from only a few input/output pairs for that task, using prior data from similar tasks for meta-learning. For example, the goal might be to classify images of a Segway after seeing only one or a few examples of a Segway, with a model that has previously seen many other types of objects. Likewise, in few-shot regression, the goal is to predict the outputs of a continuous-valued function from only a few datapoints sampled from that function, after training on many func- tions with similar statistical properties. | 1703.03400#14 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
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"id": "1508.03854"
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}
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|
1703.03429 | 14 | It is worth noting that our algorithm is not resilient to pol- ysemy, and behaves unpredictably when multiple interpre- tations exist for a given word. For example, the verb âeatâ is highly affordant with respect to most food items, but the twelve most salient results for âappleâ are {âappleâ, âpackageâ, âprogramâ, âreleaseâ, âsyncâ, âbuyâ, âoutsellâ, âdownloadâ, âin- stallâ, âreinstallâ, âuninstallâ, ârebootâ}. In this case, âApple, the software companyâ is more strongly represented in the corpus than âapple, the fruitâ.
3.2 Finding a verb that matches a given noun is useful. But an au- tonomous agent is often confronted with more than one object at a time. How should it determine which object to manipu- late, or whether any of the objects are manipulable? Pencils, | 1703.03429#14 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 15 | To formalize the supervised regression and classiï¬cation problems in the context of the meta-learning deï¬nitions in Section 2.1, we can deï¬ne the horizon H = 1 and drop the timestep subscript on xt, since the model accepts a single input and produces a single output, rather than a sequence of inputs and outputs. The task Ti generates K i.i.d. ob- servations x from qi, and the task loss is represented by the error between the modelâs output for x and the correspond- ing target values y for that observation and task.
Note that the meta-optimization is performed over the model parameters 0, whereas the objective is computed us- ing the updated model parameters 6â. In effect, our pro- posed method aims to optimize the model parameters such that one or a small number of gradient steps on a new task will produce maximally effective behavior on that task.
The meta-optimization across tasks is performed via stochastic gradient descent (SGD), such that the model pa- rameters 6 are updated as follows:
6<-8-BVo YS. Lr( fo) (1) Ti~p(T) | 1703.03400#15 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
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},
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"id": "1611.02779"
},
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"id": "1603.04467"
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|
1703.03429 | 15 | ire gat 2 gpple , eel ct 04 wad atreet 03 3 op pate 67295 gastie a yo gina ES ath âir gates = drone Bon way wglivise : pote Oe = 00 our 9 Syord e) gines 5 sina oem ARMED eovensncon 3 t B-0af ates ee ney grosquito wizard HF rach oc é39 er BPE acer HN error gyallet -0.4 | etissors 03 0.2 â01 0.0 OL 0.2 vector['forest'] - vector| treeâ)
Figure 3: Word vectors projected into the space deï¬ned by vector[âforestâ] â vector[âtreeâ] and vector[âmountainâ] â vector[âpebbleâ]. Small, manipulable objects appear in the lower-left corner of the graph. Large, abstract, or background objects appear in the upper right. An objectâs manipulabil- ity can be roughly estimated by measuring its location along either of the deï¬ning axes.
pillows, and coffee mugs are easy to grasp and lift, but the same cannot be said of shadows, boulders, or holograms. | 1703.03429#15 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 16 | 6<-8-BVo YS. Lr( fo) (1) Ti~p(T)
Two common loss functions used for supervised classiï¬ca- tion and regression are cross-entropy and mean-squared er- ror (MSE), which we will describe below; though, other su- pervised loss functions may be used as well. For regression tasks using mean-squared error, the loss takes the form:
Lr(fe)= Yo Wife) -y¥P|3, @ x), yOAT;
where x(j), y(j) are an input/output pair sampled from task Ti. In K-shot regression tasks, K input/output pairs are provided for learning for each task.
where β is the meta step size. The full algorithm, in the general case, is outlined in Algorithm 1.
The MAML meta-gradient update involves a gradient through a gradient. Computationally, this requires an addi- tional backward pass through f to compute Hessian-vector
Similarly, for discrete classification tasks with a cross- entropy loss, the loss takes the form: Lo (fe) = SD ¥ 0g folx)
LTi(fÏ) = x(j),y(j)â¼Ti + (1 â y(j)) log(1 â fÏ(x(j))) (3) | 1703.03400#16 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
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|
1703.03429 | 16 | pillows, and coffee mugs are easy to grasp and lift, but the same cannot be said of shadows, boulders, or holograms.
To identify affordant nouns - i.e. nouns that can be ma- nipulated in a meaningful way - we again utilize analogies based on canonical examples. In this section, we describe a noun as affordant to the extent that it can be pushed, pulled, grasped, transported, or transformed. After all, it would not make much sense to lift a sunset or unlock a cliff.
We begin by defining canonical affordance vectors @, = Tig1 â Tig and Gy = fy, â Tyo for each axis of the affordant vector space. Then, for each object 0; under consideration, a pair of projections po, = 0; dot @, and po, = 0; dot dy.
The results of such a projection can be seen in Figure 3. This query is distinct from those described in section 3.1 be- cause, instead of using analogies to test the relationships be- tween nouns and verbs, we are instead locating a noun on the spectrum deï¬ned by two other words. | 1703.03429#16 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 17 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Algorithm 2 MAML for Few-Shot Supervised Learning Require: p(T ): distribution over tasks Require: α, β: step size hyperparameters
Algorithm 3 MAML for Reinforcement Learning Require: p(T ): distribution over tasks Require: α, β: step size hyperparameters
1: randomly initialize θ 2: while not done do 3: 4: 5: 6:
Sample batch of tasks Ti â¼ p(T ) for all Ti do
Sample K datapoints D = {x,y} from T; Evaluate Vo £7; (fo) using D and £7, in Equation (2) or (3) Compute adapted parameters with gradient descent: 0, = 0 â aVoLr,(fo) Sample datapoints D} = {x,y} from 7; for the meta-update
7:
8:
9: 10:
end for Update θ â θ â βâθ and LTi in Equation 2 or 3
9: end for
10: Update @ â 0â BVe YF ty LT: (for) using each D/ and £7; in Equation 2 or 3
1: randomly initialize θ 2: while not done do 3: 4: 5:
Sample batch of tasks Ti â¼ p(T ) for all Ti do | 1703.03400#17 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
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"id": "1611.05763"
}
]
|
1703.03429 | 17 | In our experiments, we used a single canonical vec- tor, vector[âforestâ] - vector[âtreeâ], to distinguish between nouns of different classes. Potentially affordant nouns were projected onto this line of manipulability, with the word whose projection lay closest to âtreeâ being selected for fur- ther experimentation.
Critical to this approach is the insight that canonical word vectors are most effective when they are thought of as exem- plars rather than as descriptors. For example, vector[âforestâ] â vector[âtreeâ] and vector[âbuildingâ] â vector[âbrickâ] function reasonably well as projections for identifying manip- ulable items. vector[âbigâ] â vector[âsmallâ], on the other hand, is utterly ineffective.
Algorithm 1 Noun Selection With Affordance Detection 1: state = game response to last command 2: manipulable nouns â {} 3: for each word w â state do 4: if w is a noun then 5: 6: 7: 8: 9: end for 10: noun = a randomly selected noun from manipulable nouns | 1703.03429#17 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 18 | 1: randomly initialize θ 2: while not done do 3: 4: 5:
Sample batch of tasks Ti â¼ p(T ) for all Ti do
Sample K trajectories D = {(x1, a1, ...xx)} using fo in 7; Evaluate Vo £7; (fo) using D and £7, in Equation 4 Compute adapted parameters with gradient descent: 9; = 0 â aVoLr, (fo) Sample trajectories Dj = {(x1, a1, ...x)} using fg: in Ti end for Update @ â 8 â BV0 Yo¢. ur) £7; (for) using each Di and £7; in Equation 4
6: 7:
8:
9: 10:
9: end for
11: end while
11: end while
According to the conventional terminology, K-shot classi- ï¬cation tasks use K input/output pairs from each class, for a total of N K data points for N -way classiï¬cation. Given a distribution over tasks p(Ti), these loss functions can be di- rectly inserted into the equations in Section 2.2 to perform meta-learning, as detailed in Algorithm 2.
# 3.2. Reinforcement Learning | 1703.03400#18 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
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},
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"id": "1611.02779"
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"id": "1603.04467"
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}
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|
1703.03429 | 18 | Algorithm 2 Verb Selection With Analogy Reduction 1: navigation verbs = [ânorthâ, âsouthâ, âeastâ, âwestâ, ânortheastâ, âsoutheastâ,
âsouthwestâ, ânorthwestâ, âupâ, âdownâ, âenterâ]
2: manipulation verbs = a list of 1000 most common verbs 3: essential manipulation verbs = [âgetâ, âdropâ, âpushâ, âpullâ, âopenâ,
âcloseâ]
4: aï¬ordant verbs = verbs returned by Word2vec that match noun 5: aï¬ordant verbs = aï¬ordant verbs â©
manipulation verbs
6: f inal verbs = navigation verbs ⪠aï¬ordant verbs ⪠essential manipulation verbs
7: verb = a randomly selected verb from f inal verbs | 1703.03429#18 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 19 | # 3.2. Reinforcement Learning
In reinforcement learning (RL), the goal of few-shot meta- learning is to enable an agent to quickly acquire a policy for a new test task using only a small amount of experience in the test setting. A new task might involve achieving a new goal or succeeding on a previously trained goal in a new environment. For example, an agent might learn to quickly ï¬gure out how to navigate mazes so that, when faced with a new maze, it can determine how to reliably reach the exit with only a few samples. In this section, we will discuss how MAML can be applied to meta-learning for RL. | 1703.03400#19 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
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}
]
|
1703.03429 | 19 | manipulation verbs
6: f inal verbs = navigation verbs ⪠aï¬ordant verbs ⪠essential manipulation verbs
7: verb = a randomly selected verb from f inal verbs
4 Test Environment: A World Made of Words In this paper, we test our ideas in the challenging world of text-based adventure gaming. Text-based adventure games offer an unrestricted, free-form interface: the player is pre- sented with a block of text describing a situation, and must respond with a written phrase. Typical actions include com- mands such as: âexamine walletâ, âeat appleâ, or âlight camp- ï¬re with matchesâ. The game engine parses this response and produces a new block of text. The resulting inter- actions, although syntactically simple, provide a fertile re- search environment for natural language processing and hu- man/computer interaction. Game players must identify ob- jects that are manipulable and apply appropriate actions to those objects in order to make progress. | 1703.03429#19 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 20 | Since the expected reward is generally not differentiable due to unknown dynamics, we use policy gradient meth- ods to estimate the gradient both for the model gradient update(s) and the meta-optimization. Since policy gradi- ents are an on-policy algorithm, each additional gradient step during the adaptation of fg requires new samples from the current policy fo,,. We detail the algorithm in Algo- rithm 3. This algorithm has the same structure as Algo- rithm 2, with the principal difference being that steps 5 and 8 require sampling trajectories from the environment cor- responding to task 7;. Practical implementations of this method may also use a variety of improvements recently proposed for policy gradient algorithms, including state or action-dependent baselines and trust regions (Schulman et al., 2015).
# 4. Related Work | 1703.03400#20 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
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"id": "1603.04467"
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|
1703.03429 | 20 | In these games, the learning agent faces a frustrating di- chotomy: its action set must be large enough to accommodate any situation it encounters, and yet each additional action in- creases the size of its search space. A brute force approach to such scenarios is frequently futile, and yet factorization, func- tion approximation, and other search space reduction tech- niques bring the risk of data loss. We desire an agent that is able to clearly perceive all its options, and yet applies only that subset which is likely to produce results.
In other words, we want an agent that explores the game world the same way a human does: by trying only those ac- tions that âmake senseâ. In the following sections, we show that affordance-based action selection provides a meaningful ï¬rst step towards this goal.
4.1 Learning algorithm Our agent utilizes a variant of Q-learning [Watkins and Dayan, 1992], a reinforcement learning algorithm which at- tempts to maximize expected discounted reward. Q-values are updated according to the equation
AQ(s, a) = a(R(s, a) + ymaraQ(sâ,a) â Q(s,a)) (1) where Q(s, a) is the expected reward for performing action a in observed state s, a is the learning rate, 7 is the discount | 1703.03429#20 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 21 | # 4. Related Work
Each RL task Ti contains an initial state distribution qi(x1) and a transition distribution qi(xt+1|xt, at), and the loss LTi corresponds to the (negative) reward function R. The entire task is therefore a Markov decision process (MDP) with horizon H, where the learner is allowed to query a limited number of sample trajectories for few-shot learn- ing. Any aspect of the MDP may change across tasks in p(T ). The model being learned, fθ, is a policy that maps from states xt to a distribution over actions at at each timestep t â {1, ..., H}. The loss for task Ti and model fÏ takes the form
H Lai (fo) = âExi ain fo.a7; > a) - 4 t=1 | 1703.03400#21 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
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|
1703.03429 | 21 | Figure 4: Sample text from the adventure game Zork. Player responses follow a single angle bracket.
factor, and sâ is the new state observation after performing action a. Because our test environments are typically deter- ministic with a high percentage of consumable rewards, we modify this algorithm slightly, setting a = 1 and constrain- ing Q-value updates such that
Q'(s,a) = max( Q(s,a), Q(s,a) + AQ(s,a)) (2)
This adaptation encourages the agent to retain behaviors that have produced a reward at least once, even if the reward fails to manifest on subsequent attempts. The goal is to prevent the agent from âunlearningâ behaviors that are no longer effective during the current training epoch, but which will be essential in order to score points during the next round of play.
The agentâs state representation is encoded as a hash of the text provided by the game engine. Actions are comprised of verb/object pairs:
a=v+ââ +0,veV,0cO (3) | 1703.03429#21 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 22 | H Lai (fo) = âExi ain fo.a7; > a) - 4 t=1
The method that we propose in this paper addresses the general problem of meta-learning (Thrun & Pratt, 1998; Schmidhuber, 1987; Naik & Mammone, 1992), which in- cludes few-shot learning. A popular approach for meta- learning is to train a meta-learner that learns how to up- date the parameters of the learnerâs model (Bengio et al., 1992; Schmidhuber, 1992; Bengio et al., 1990). This ap- proach has been applied to learning to optimize deep net- works (Hochreiter et al., 2001; Andrychowicz et al., 2016; Li & Malik, 2017), as well as for learning dynamically changing recurrent networks (Ha et al., 2017). One recent approach learns both the weight initialization and the opti- mizer, for few-shot image recognition (Ravi & Larochelle, 2017). Unlike these methods, the MAML learnerâs weights are updated using the gradient, rather than a learned update; our method does not introduce additional parameters for meta-learning nor require a particular learner architecture. | 1703.03400#22 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
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|
1703.03429 | 22 | a=v+ââ +0,veV,0cO (3)
where V is the set of all English-language verbs and O is the set of all English-language nouns. To enable the agent to dis- tinguish between state transitions and merely informational feedback, the agent executes a âlookâ command every second iteration and assumes that the resulting game text represents its new state. Some games append a summary of actions taken and points earned in response to each âlookâ command. To prevent this from obfuscating the state space, we stripped all numerals from the game text prior to hashing.
Given that the English language contains at least 20,000 verbs and 100,000 nouns in active use, a naive application of Q-learning is intractable. Some form of action-space reduc- tion must be used. For our baseline comparison, we use an agent with a vocabulary consisting of the 1000 most common verbs in Wikipedia, an 11-word navigation list and a 6-word essential manipulation list as depicted in Algorithm 2. The navigation list contains words which, by convention, are used to navigate through text-based games. The essential manip- ulation list contains words which, again by convention, are generally applicable to all in-game objects. | 1703.03429#22 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 23 | In K-shot reinforcement learning, K rollouts from fθ and task Ti, (x1, a1, ...xH ), and the corresponding rewards R(xt, at), may be used for adaptation on a new task Ti.
Few-shot learning methods have also been developed for
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
speciï¬c tasks such as generative modeling (Edwards & Storkey, 2017; Rezende et al., 2016) and image recogni- tion (Vinyals et al., 2016). One successful approach for few-shot classiï¬cation is to learn to compare new exam- ples in a learned metric space using e.g. Siamese net- works (Koch, 2015) or recurrence with attention mech- anisms (Vinyals et al., 2016; Shyam et al., 2017; Snell et al., 2017). These approaches have generated some of the most successful results, but are difï¬cult to directly extend to other problems, such as reinforcement learning. Our method, in contrast, is agnostic to the form of the model and to the particular learning task.
model learned with MAML continue to improve with addi- tional gradient updates and/or examples? | 1703.03400#23 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
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}
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|
1703.03429 | 23 | The baseline agent does not use a ï¬xed noun vocabulary. Instead, it extracts nouns from the game text using part-of- speech tags. To facilitate game interactions, the baseline agent augments its noun list using adjectives that precede them. For example, if the game text consisted of âYou see a red pill and a blue pillâ, then the agentâs noun list for that
superior performance detective cavetrip curses mansion comparable performance inferior performance break-in omniquest ou fae Parc zenon parallel reverb spirit ztuu we 7S â ? bs KO 5 y ââ 20 : 1 b 10 : candy zork1 tryst205 om a0 aoa) 1000 > am ane ano m0 ion0 om âa0 0a 1000 u 10 â baseline agent oe â verb space reduction oe â object space reduction â verb and object reduction
Figure 5: Learning trajectories for sixteen Z-machine games. Agents played each game 1000 times, with 1000 game steps during each trial. No agent received any reward on the remaining 32 games. 10 data runs were averaged to create this plot.
state would be [âpillâ, âred pillâ, âblue pillâ]. (And its next action is hopefully âswallow red pillâ). | 1703.03429#23 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 24 | model learned with MAML continue to improve with addi- tional gradient updates and/or examples?
All of the meta-learning problems that we consider require some amount of adaptation to new tasks at test-time. When possible, we compare our results to an oracle that receives the identity of the task (which is a problem-dependent rep- resentation) as an additional input, as an upper bound on the performance of the model. All of the experiments were performed using TensorFlow (Abadi et al., 2016), which al- lows for automatic differentiation through the gradient up- date(s) during meta-learning. The code is available online1. | 1703.03400#24 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
"id": "1612.00796"
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"id": "1603.04467"
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|
1703.03429 | 24 | state would be [âpillâ, âred pillâ, âblue pillâ]. (And its next action is hopefully âswallow red pillâ).
In Sections 5.1 and 5.2 the baseline agent is contrasted with an agent using affordance extraction to reduce its manipula- tion list from 1000 verbs to a mere 30 verbs for each state, and to reduce its object list to a maximum of 15 nouns per state. We compare our approach to other search space reduc- tion techniques and show that the a priori knowledge pro- vided by affordance extraction enables the agent to achieve results which cannot be paralleled through brute force meth- ods. All agents used epsilon-greedy exploration with a de- caying epsilon.
The purpose of our research was to test the value of affordance-based search space reduction. Therefore, we did not add augmentations to address some of the more challeng- ing aspects of text-based adventure games. Speciï¬cally, the agent maintained no representation of items carried in inven- tory or of the game score achieved thus far. The agent was also not given the ability to construct prepositional commands such as âput book on shelfâ or âslay dragon with swordâ. | 1703.03429#24 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 25 | Another approach to meta-learning is to train memory- augmented models on many tasks, where the recurrent learner is trained to adapt to new tasks as it is rolled out. Such networks have been applied to few-shot image recog- nition (Santoro et al., 2016; Munkhdalai & Yu, 2017) and learning âfastâ reinforcement learning agents (Duan et al., 2016b; Wang et al., 2016). Our experiments show that our method outperforms the recurrent approach on few- shot classiï¬cation. Furthermore, unlike these methods, our approach simply provides a good weight initialization and uses the same gradient descent update for both the learner and meta-update. As a result, it is straightforward to ï¬ne- tune the learner for additional gradient steps. | 1703.03400#25 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
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"id": "1603.04467"
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"id": "1508.03854"
},
{
"id": "1611.05763"
}
]
|
1703.03429 | 25 | Our affordance-based search space reduction algorithms enabled the agent to score points on 16/50 games, with a peak performance (expressed as a percentage of maximum game score) of 23.40% for verb space reduction, 4.33% for object space reduction, and 31.45% when both methods were combined. The baseline agent (see Sec. 4.1) scored points on 12/50 games, with a peak performance of 4.45%. (Peak performance is deï¬ned as the maximum score achieved over all epochs, a metric that expresses the agentâs ability to comb through the search space and discover areas of high reward.)
Two games experienced termination errors and were ex- cluded from our subsequent analysis; however, our reduction methods outperformed the baseline in both peak performance and average reward in the discarded partial results.
Figures 5 and 7 show the performance of our reduction techniques when compared to the baseline. Affordance- based search space reduction improved overall performance on 12/16 games, and decreased performance on only 1 game. | 1703.03429#25 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 26 | Our approach is also related to methods for initialization of deep networks. In computer vision, models pretrained on large-scale image classiï¬cation have been shown to learn effective features for a range of problems (Donahue et al., In contrast, our method explicitly optimizes the 2014). model for fast adaptability, allowing it to adapt to new tasks with only a few examples. Our method can also be viewed as explicitly maximizing sensitivity of new task losses to the model parameters. A number of prior works have ex- plored sensitivity in deep networks, often in the context of initialization (Saxe et al., 2014; Kirkpatrick et al., 2016). Most of these works have considered good random initial- izations, though a number of papers have addressed data- dependent initializers (Kr¨ahenb¨uhl et al., 2016; Salimans & Kingma, 2016), including learned initializations (Husken & Goerick, 2000; Maclaurin et al., 2015). In contrast, our method explicitly trains the parameters for sensitivity on a given task distribution, allowing for extremely efï¬cient adaptation for problems such as K-shot learning and rapid reinforcement learning in only one or a few gradient steps.
# 5. Experimental Evaluation | 1703.03400#26 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
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"id": "1603.04467"
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}
]
|
1703.03429 | 26 | 5 Results We tested our agent on a suite of 50 text-based adventure games compatible with Infocomâs Z-machine. These games represent a wide variety of situations, ranging from business scenarios like âDetectiveâ to complex ï¬ctional worlds like âZork: The Underground Empireâ. Signiï¬cantly, the games provide little or no information about the agentâs goals, or actions that might provide reward.
During training, the agent interacted with the game engine for 1000 epochs, with 1000 training steps in each epoch. On each game step, the agent received a positive reward corre- sponding to the change in game score. At the end of each epoch the game was restarted and the game score reset, but the agent retained its learned Q-values. | 1703.03429#26 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03429 | 27 | Examination of the 32 games in which no agent scored points (and which are correspondingly not depicted in Fig- ures 5 and 7) revealed three prevalent failure modes: (1) The game required prepositional commands such as âlook at ma- chineâ or âgive dagger to wizardâ, (2) The game provided points only after an unusually complex sequence of events, (3) The game required the user to infer the proper term for manipulable objects. (For example, the game might describe âsomething shinyâ at the bottom of a lake, but required the agent to âget shiny objectâ.) Our test framework was not de- signed to address these issues, and hence did not score points on those games. A fourth failure mode (4) might be the ab- sence of a game-critical verb within the 1000-word manipu- lation list. However, this did not occur in our coarse exami- nation of games that failed.
Affordant selection Random selection decorate glass open window add table generate quantity ring window weld glass travel passage climb staircase jump table
Figure 6: Sample exploration actions produced by a Q-learner with and without affordance detection. The random agent used nouns extracted from game text and a verb list compris- ing the 200 most common verbs in Wikipedia. | 1703.03429#27 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 28 | We start with a simple regression problem that illustrates the basic principles of MAML. Each task involves regress- ing from the input to the output of a sine wave, where the amplitude and phase of the sinusoid are varied between tasks. Thus, p(T ) is continuous, where the amplitude varies within [0.1, 5.0] and the phase varies within [0, Ï], and the input and output both have a dimensionality of 1. During training and testing, datapoints x are sampled uni- formly from [â5.0, 5.0]. The loss is the mean-squared error between the prediction f (x) and true value. The regres- sor is a neural network model with 2 hidden layers of size 40 with ReLU nonlinearities. When training with MAML, we use one gradient update with K = 10 examples with a ï¬xed step size α = 0.01, and use Adam as the meta- optimizer (Kingma & Ba, 2015). The baselines are like- wise trained with Adam. To evaluate performance, we ï¬ne- tune a single meta-learned model on varying numbers of K examples, and compare performance to two baselines: (a) pretraining on all of the | 1703.03400#28 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
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}
]
|
1703.03429 | 28 | 5.1 Alternate reduction methods We compared our affordance-based reduction technique with four other approaches that seemed intuitively applicable to the test domain. Results are shown in Figure 7.
Intrinsic rewards: This approach guides the agentâs ex- ploration of the search space by allotting a small reward each time a new state is attained. We call these awards intrinsic because they are tied to the agentâs assessment of its progress rather than to external events.
Random reduction: When applying search space reduc- tions one must always ask: âDid improvements result from my speciï¬c choice of reduced space, or would any reduction be equally effective?â We address this question by randomly selecting 30 manipulation verbs to use during each epoch. | 1703.03429#28 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 29 | tune a single meta-learned model on varying numbers of K examples, and compare performance to two baselines: (a) pretraining on all of the tasks, which entails training a net- work to regress to random sinusoid functions and then, at test-time, ï¬ne-tuning with gradient descent on the K pro- vided points, using an automatically tuned step size, and (b) an oracle which receives the true amplitude and phase as input. In Appendix C, we show comparisons to addi- tional multi-task and adaptation methods. | 1703.03400#29 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
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|
1703.03429 | 29 | ConceptNet reduction: In this approach we used Con- ceptNetâs CapableOf relation to obtain a list of verbs relevant to the current object. We then reduced the agentâs manipula- tion list to include only words that were also in ConceptNetâs word list (effectively taking the intersection of the two lists). Co-occurrence reduction: In this method, we populated a co-occurrence dictionary using the 1000 most common verbs and 30,000 most common nouns in Wikipedia. The dictio- nary tracked the number of times each verb/noun pair oc- curred within a 9-word radius. During game play, the agentâs manipulation list was reduced to include only words which exceeded a low threshold (co-occurrences > 3).
Figure 7 shows the performance of these four algorithms, along with a baseline learner using a 1000-word manipulation list. Affordance-based verb selection improved performance in most games, but the other reduction techniques fell prey to a classic danger: they pruned precisely those actions which were essential to obtain reward.
# 5.2 Fixed-length vocabularies vs. Free-form learning
An interesting question arises from our research. What if, rather than beginning with a 1000-word vocabulary, the agent was free to search the entire English-language verb space? | 1703.03429#29 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 30 | We evaluate performance by ï¬ne-tuning the model learned by MAML and the pretrained model on K = {5, 10, 20} datapoints. During ï¬ne-tuning, each gradient step is com- puted using the same K datapoints. The qualitative results, shown in Figure 2 and further expanded on in Appendix B show that the learned model is able to quickly adapt with only 5 datapoints, shown as purple triangles, whereas the model that is pretrained using standard supervised learning on all tasks is unable to adequately adapt with so few dat- apoints without catastrophic overï¬tting. Crucially, when the K datapoints are all in one half of the input range, the
1Code for the regression and supervised experiments is at github.com/cbfinn/maml and code for the RL experi- ments is at github.com/cbfinn/maml_rl
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
MAML, K=5 MAML, K=10 pretrained, K=5, step size=0.01 retrained, K=10, step size=0.02
retrained, K=10, step size=0.02
pretrained, K=5, step size=0.01
MAML, K=5
MAML, K=10 | 1703.03400#30 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
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|
1703.03429 | 30 | An interesting question arises from our research. What if, rather than beginning with a 1000-word vocabulary, the agent was free to search the entire English-language verb space?
A traditional learning agent could not do this: the space of possible verbs is too large. However, the Wikipedia knowl- edge base opens new opportunities. Using the action selec1.0 [candy EME detective EE omnniquest mmm 2en0n ° ° ° iS & & normalized average score 2 S 0.0 ¢ * é & 8 § sg, £. o 2 & g 58 os e 3 é S oe s& g os g ? gs és gg ¥ 5 SF ss es ¢ & es ge se s 8 é © & & > &
Figure 7: Five verb space reduction techniques compared over 100 exploration epochs. Average of 5 data runs. Re- sults were normalized for each game based on the maximum reward achieved by any agent.
tion mechanism described in Section 4.1, we allowed the agent to construct its own manipulation list for each state (see Section 3.1). The top 15 responses were unioned with the agentâs navigation and essential manipulation lists, with actions selected randomly from that set. | 1703.03429#30 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 31 | retrained, K=10, step size=0.02
pretrained, K=5, step size=0.01
MAML, K=5
MAML, K=10
pre-update lgradstep --+ 10 grad steps â- groundtruth « 4 used for grad lgradstep <= 10 grad steps \ pre-update
Figure 2. Few-shot adaptation for the simple regression task. Left: Note that MAML is able to estimate parts of the curve where there are no datapoints, indicating that the model has learned about the periodic structure of sine waves. Right: Fine-tuning of a model pretrained on the same distribution of tasks without MAML, with a tuned step size. Due to the often contradictory outputs on the pre-training tasks, this model is unable to recover a suitable representation and fails to extrapolate from the small number of test-time samples.
k-shot regression, k=10 â*â MAMI (ours) = *- pretrained, step=0.02 sor oracle mean squared error number of gradient steps
Figure 3. Quantitative sinusoid regression results showing the learning curve at meta test-time. Note that MAML continues to improve with additional gradient steps without overï¬tting to the extremely small dataset during meta-testing, achieving a loss that is substantially lower than the baseline ï¬ne-tuning approach. | 1703.03400#31 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
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|
1703.03429 | 31 | A sampling of the agentâs behavior is displayed in Figure 6, along with comparable action selections from the baseline agent described in Section 4.1. The free-form learner is able to produce actions that seem, not only reasonable, but also rather inventive when considered in the context of the game environment. We believe that further research in this direction may enable the development of one-shot learning for text- based adventure games.
6 Conclusion The common sense knowledge implicitly encoded within Wikipedia opens new opportunities for autonomous agents. In this paper we have shown that previously intractable search spaces can be efï¬ciently navigated when word embeddings are used to identify context-dependent affordances. In the do- main of text-based adventure games, this approach is superior to several other intuitive methods.
Our initial experiments have been restricted to text-based environments, but the underlying principles apply to any do- main in which mappings can be formed between words and objects. Steady advances in object recognition and semantic segmentation, combined with improved precision in robotic systems, suggests that our methods are applicable to systems including self-driving cars, domestic robots, and UAVs. | 1703.03429#31 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 32 | model trained with MAML can still infer the amplitude and phase in the other half of the range, demonstrating that the MAML trained model f has learned to model the periodic nature of the sine wave. Furthermore, we observe both in the qualitative and quantitative results (Figure 3 and Ap- pendix B) that the model learned with MAML continues to improve with additional gradient steps, despite being trained for maximal performance after one gradient step. This improvement suggests that MAML optimizes the pa- rameters such that they lie in a region that is amenable to fast adaptation and is sensitive to loss functions from p(T ), as discussed in Section 2.2, rather than overï¬tting to pa- rameters θ that only improve after one step.
# 5.2. Classiï¬cation | 1703.03400#32 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
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}
]
|
1703.03429 | 32 | 7 Acknowledgements Our experiments were run using Autoplay: a learn- ing environment for interactive ï¬ction (https://github.com/- danielricks/autoplay). We thank Nvidia, the Center for Un- manned Aircraft Systems, and Analog Devices, Inc. for their generous support.
# References [Arkin, 1998] Ronald C. Arkin. Behavior-Based Robotics. MIT
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[Bolukbasi et al., 2016a] Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, NIPS, pages 4349â4357. Curran Associates, Inc., 2016.
[Bolukbasi et al., 2016b] Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, and Adam Tauman Kalai. Quantifying and reducing stereotypes in word embeddings. CoRR, abs/1606.06121, 2016. | 1703.03429#32 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 33 | # 5.2. Classiï¬cation
To evaluate MAML in comparison to prior meta-learning and few-shot learning algorithms, we applied our method to few-shot image recognition on the Omniglot (Lake et al., 2011) and MiniImagenet datasets. The Omniglot dataset consists of 20 instances of 1623 characters from 50 dif- ferent alphabets. Each instance was drawn by a different person. The MiniImagenet dataset was proposed by Ravi & Larochelle (2017), and involves 64 training classes, 12 validation classes, and 24 test classes. The Omniglot and MiniImagenet image recognition tasks are the most com- mon recently used few-shot learning benchmarks (Vinyals et al., 2016; Santoro et al., 2016; Ravi & Larochelle, 2017). | 1703.03400#33 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
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},
{
"id": "1611.02779"
},
{
"id": "1603.04467"
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},
{
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},
{
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}
]
|
1703.03429 | 33 | Prodromos Malakasiotis, and Ion Androutsopoulos. Using centroids of word embeddings and word moverâs distance for biomedical document retrieval in question answering. CoRR, abs/1608.03905, 2016. [Frome et al., 2013] Andrea Frome, Greg S. Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, and Tomas Mikolov. Devise: A deep visual-semantic embedding model. In In NIPS, 2013. [Gibson, 1977] James J. Gibson. The theory of affordances.
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[Hochreiter and Schmidhuber, 1997] Sepp Hochreiter and J¨urgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735â1780, 1997.
and Li Fei-fei. Deep fragment embeddings for bidirectional image sentence mapping. In In arXiv:1406.5679, 2014. | 1703.03429#33 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 34 | We follow the experimental protocol proposed by Vinyals et al. (2016), which involves fast learning of N -way clas- siï¬cation with 1 or 5 shots. The problem of N -way classi- ï¬cation is set up as follows: select N unseen classes, pro- vide the model with K different instances of each of the N classes, and evaluate the modelâs ability to classify new in- stances within the N classes. For Omniglot, we randomly select 1200 characters for training, irrespective of alphabet, and use the remaining for testing. The Omniglot dataset is augmented with rotations by multiples of 90 degrees, as proposed by Santoro et al. (2016). | 1703.03400#34 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
"id": "1612.00796"
},
{
"id": "1611.02779"
},
{
"id": "1603.04467"
},
{
"id": "1703.05175"
},
{
"id": "1508.03854"
},
{
"id": "1611.05763"
}
]
|
1703.03429 | 34 | and Li Fei-fei. Deep fragment embeddings for bidirectional image sentence mapping. In In arXiv:1406.5679, 2014.
[Kiros et al., 2015] Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, and Sanja Fidler. Skip-thought vectors. CoRR, abs/1506.06726, 2015. [Laird and van Lent, 2001] John E. Laird and Michael van Lent. Human-level AIâs killer application: Interactive computer games. AI Magazine, 22(2):15â26, 2001.
[Le and Mikolov, 2014] Quoc V. Le and Tomas Mikolov. Dis- tributed representations of sentences and documents. CoRR, abs/1405.4053, 2014.
[Liu and Singh, 2004] H. Liu and P. Singh. Conceptnet â a prac- tical commonsense reasoning tool-kit. BT Technology Journal, 22(4):211â226, 2004. | 1703.03429#34 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 35 | Our model follows the same architecture as the embedding function used by Vinyals et al. (2016), which has 4 mod- ules with a 3 à 3 convolutions and 64 ï¬lters, followed by batch normalization (Ioffe & Szegedy, 2015), a ReLU non- linearity, and 2 à 2 max-pooling. The Omniglot images are downsampled to 28 à 28, so the dimensionality of the last hidden layer is 64. As in the baseline classiï¬er used by Vinyals et al. (2016), the last layer is fed into a soft- max. For Omniglot, we used strided convolutions instead of max-pooling. For MiniImagenet, we used 32 ï¬lters per layer to reduce overï¬tting, as done by (Ravi & Larochelle, 2017). In order to also provide a fair comparison against memory-augmented neural networks (Santoro et al., 2016) and to test the ï¬exibility of MAML, we also provide re- sults for a non-convolutional network. For this, we use a network with 4 hidden layers with sizes 256, 128, 64, 64, each including batch normalization and ReLU nonlineari- | 1703.03400#35 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
"id": "1612.00796"
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"id": "1508.03854"
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|
1703.03429 | 35 | [Matuszek et al., 2006] Cynthia Matuszek, John Cabral, Michael Witbrock, and John Deoliveira. An introduction to the syntax and content of cyc. In Proceedings of the 2006 AAAI Spring Sympo- sium on Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question An- swering, pages 44â49, 2006.
[Mikolov et al., 2013a] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efï¬cient estimation of word representations in vector space. CoRR, abs/1301.3781, 2013.
[Mikolov et al., 2013b] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, editors, NIPS, pages 3111â3119. Curran Associates, Inc., 2013. [Mikolov et al., 2013c] Tomas Mikolov, Wen tau Yih, and Geoffrey Zweig. Linguistic regularities in continuous space word represen- tations. Association for Computational Linguistics, May 2013. | 1703.03429#35 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
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1703.03429 | 36 | [Miller, 1995] George A. Miller. Wordnet: A lexical database for english. Commun. ACM, 38(11):39â41, November 1995.
[Mnih et al., 2015] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostro- vski, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529â533, 2015.
[Montesano et al., 2007] L. Montesano, M. Lopes, A. Bernardino, and J. Santos-Victor. Modeling affordances using bayesian net- works. In 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 4102â4107, Oct 2007.
[Narasimhan et al., 2015] Karthik Narasimhan, Tejas D. Kulka- rni, and Regina Barzilay. Language understanding for text- CoRR, based games using deep reinforcement abs/1506.08941, 2015. | 1703.03429#36 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 37 | We present the results in Table 1. The convolutional model learned by MAML compares well to the state-of-the-art re- sults on this task, narrowly outperforming the prior meth- ods. Some of these existing methods, such as matching networks, Siamese networks, and memory models are de- signed with few-shot classiï¬cation in mind, and are not readily applicable to domains such as reinforcement learn- ing. Additionally, the model learned with MAML uses
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Table 1. Few-shot classiï¬cation on held-out Omniglot characters (top) and the MiniImagenet test set (bottom). MAML achieves results that are comparable to or outperform state-of-the-art convolutional and recurrent models. Siamese nets, matching nets, and the memory module approaches are all speciï¬c to classiï¬cation, and are not directly applicable to regression or RL scenarios. The ± shows 95% conï¬dence intervals over tasks. Note that the Omniglot results may not be strictly comparable since the train/test splits used in the prior work were not available. The MiniImagenet evaluation of baseline methods and matching networks is from Ravi & Larochelle (2017). | 1703.03400#37 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
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}
]
|
1703.03429 | 37 | [Navarro et al., 2012] Stefan Escaida Navarro, Nicolas Gorges, Heinz W¨orn, Julian Schill, Tamim Asfour, and R¨udiger Dill- mann. Haptic object recognition for multi-ï¬ngered robot hands. In 2012 IEEE Haptics Symposium (HAPTICS), pages 497â502. IEEE, 2012.
[Russ et al., 2011] Thomas A Russ, Cartic Ramakrishnan, Ed- uard H Hovy, Mihail Bota, and Gully APC Burns. Knowledge engineering tools for reasoning with scientiï¬c observations and interpretations: a neural connectivity use case. BMC bioinfor- matics, 12(1):351, 2011. | 1703.03429#37 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 38 | 5-way Accuracy 20-way Accuracy Omniglot (Lake et al., 2011) MANN, no conv (Santoro et al., 2016) MAML, no conv (ours) Siamese nets (Koch, 2015) matching nets (Vinyals et al., 2016) neural statistician (Edwards & Storkey, 2017) memory mod. (Kaiser et al., 2017) MAML (ours) 1-shot 82.8% 5-shot 94.9% 89.7 ± 1.1% 97.5 ± 0.6% 97.3% 98.1% 98.1% 98.4% 98.4% 98.9% 99.5% 99.6% 1-shot â â 88.2% 93.8% 93.2% 95.0% 5-shot â â 97.0% 98.5% 98.1% 98.6% 98.7 ± 0.4% 99.9 ± 0.1% 95.8 ± 0.3% 98.9 ± 0.2% | 1703.03400#38 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
"id": "1612.00796"
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"id": "1611.02779"
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"id": "1603.04467"
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"id": "1703.05175"
},
{
"id": "1508.03854"
},
{
"id": "1611.05763"
}
]
|
1703.03429 | 38 | [Schenck et al., 2012] Wolfram Schenck, Hendrik Hasenbein, and Ralf M¨oller. Detecting affordances by mental imagery. In Alessandro G. Di Nuovo, Vivian M. de la Cruz, and Davide Marocco, editors, Proceedings of the SAB Workshop on âArti- ï¬cial Mental Imageryâ, pages 15â18, Odense (Danmark), 2012. [Schenck et al., 2016] Wolfram Schenck, Hendrik Hasenbein, and Ralf M¨oller. Detecting affordances by visuomotor simulation. arXiv preprint arXiv:1611.00274, 2016.
[Socher et al., 2011] Richard Socher, Cliff C. Lin, Chris Manning, and Andrew Y Ng. Parsing natural scenes and natural language with recursive neural networks. ICML, pages 129 â136, 2011. [Song et al., 2011] Hyun Oh Song, Mario Fritz, Chunhui Gu, and Trevor Darrell. Visual grasp affordances from appearance-based cues. In ICCV Workshops, pages 998â1005. IEEE, 2011. | 1703.03429#38 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 39 | 5-way Accuracy MiniImagenet (Ravi & Larochelle, 2017) ï¬ne-tuning baseline nearest neighbor baseline matching nets (Vinyals et al., 2016) meta-learner LSTM (Ravi & Larochelle, 2017) MAML, ï¬rst order approx. (ours) MAML (ours) 5-shot 1-shot 49.79 ± 0.79% 28.86 ± 0.54% 51.04 ± 0.65% 41.08 ± 0.70% 55.31 ± 0.73% 43.56 ± 0.84% 43.44 ± 0.77% 60.60 ± 0.71% 48.07 ± 1.75% 63.15 ± 0.91% 48.70 ± 1.84% 63.11 ± 0.92% | 1703.03400#39 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
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}
]
|
1703.03429 | 39 | [Song et al., 2015] Hyun Oh Song, Mario Fritz, Daniel Goehring, and Trevor Darrell. Learning to detect visual grasp affordance. In IEEE Transactions on Automation Science and Engineering (TASE), 2015.
[Stoytchev, 2008] Alexander Stoytchev. Learning the Affordances of Tools Using a Behavior-Grounded Approach, pages 140â158. Springer Berlin Heidelberg, Berlin, Heidelberg, 2008.
[Watkins and Dayan, 1992] Christopher JCH Watkins and Peter Dayan. Q-learning. Machine learning, 8(3-4):279â292, 1992. [Zhu et al., 2014] Yuke Zhu, Alireza Fathi, and Li Fei-Fei. Reason- ing about object affordances in a knowledge base representation. In ECCV, 2014.
[Zhu et al., 2015] Yukun Zhu, Ryan Kiros, Richard S. Zemel, Rus- lan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. CoRR, abs/1506.06724, 2015. | 1703.03429#39 | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | http://arxiv.org/pdf/1703.03429 | Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate | cs.AI, cs.CL | 7 pages, 7 figures, 2 algorithms, data runs were performed using the
Autoplay learning environment for interactive fiction | Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence (IJCAI), Pages 1039-1045, 2017 | cs.AI | 20170309 | 20170309 | [
{
"id": "1611.00274"
}
]
|
1703.03400 | 40 | fewer overall parameters compared to matching networks and the meta-learner LSTM, since the algorithm does not introduce any additional parameters beyond the weights of the classiï¬er itself. Compared to these prior methods, memory-augmented neural networks (Santoro et al., 2016) speciï¬cally, and recurrent meta-learning models in gen- eral, represent a more broadly applicable class of meth- ods that, like MAML, can be used for other tasks such as reinforcement learning (Duan et al., 2016b; Wang et al., 2016). However, as shown in the comparison, MAML sig- niï¬cantly outperforms memory-augmented networks and the meta-learner LSTM on 5-way Omniglot and MiniIm- agenet classiï¬cation, both in the 1-shot and 5-shot case. | 1703.03400#40 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
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"id": "1611.02779"
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"id": "1603.04467"
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|
1703.03400 | 41 | A significant computational expense in MAML comes from the use of second derivatives when backpropagat- ing the meta-gradient through the gradient operator in the meta-objective (see Equation (1)). On Minilmagenet, we show a comparison to a first-order approximation of MAML, where these second derivatives are omitted. Note that the resulting method still computes the meta-gradient at the post-update parameter values 0, which provides for effective meta-learning. Surprisingly however, the perfor- mance of this method is nearly the same as that obtained with full second derivatives, suggesting that most of the improvement in MAML comes from the gradients of the objective at the post-update parameter values, rather than the second order updates from differentiating through the gradient update. Past work has observed that ReLU neu- ral networks are locally almost linear (Goodfellow et al., 2015), which suggests that second derivatives may be close to zero in most cases, partially explaining the good perforpoint robot, 2d navigation WANE Gus) pretrained + random -10! += oracle average return (log scale) 1 2 number of gradient steps MAML as pre-update pre-update oa â 3steps oa 3 âkk goal position |) °2 oa a ool] â 3steps pretrained -o| 21} |e A goal position | 1703.03400#41 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
"id": "1612.00796"
},
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"id": "1611.02779"
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{
"id": "1603.04467"
},
{
"id": "1703.05175"
},
{
"id": "1508.03854"
},
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}
]
|
1703.03400 | 42 | point robot, 2d navigation WANE Gus) pretrained + random -10! += oracle average return (log scale) 1 2 number of gradient steps
MAML as pre-update oa â 3steps 3 âkk goal position |) oa a -o|
pre-update oa °2 ool] â 3steps pretrained 21} |e A goal position
Figure 4. Top: quantitative results from 2D navigation task, Bot- tom: qualitative comparison between model learned with MAML and with ï¬ne-tuning from a pretrained network.
mance of the ï¬rst-order approximation. This approxima- tion removes the need for computing Hessian-vector prod- ucts in an additional backward pass, which we found led to roughly 33% speed-up in network computation.
# 5.3. Reinforcement Learning | 1703.03400#42 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
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},
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"id": "1611.02779"
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"id": "1603.04467"
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{
"id": "1508.03854"
},
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}
]
|
1703.03400 | 43 | # 5.3. Reinforcement Learning
To evaluate MAML on reinforcement learning problems, we constructed several sets of tasks based off of the sim- ulated continuous control environments in the rllab bench- mark suite (Duan et al., 2016a). We discuss the individual domains below. In all of the domains, the model trained by MAML is a neural network policy with two hidden lay- ers of size 100, with ReLU nonlinearities. The gradient updates are computed using vanilla policy gradient (RE- INFORCE) (Williams, 1992), and we use trust-region pol- icy optimization (TRPO) as the meta-optimizer (Schulman et al., 2015). In order to avoid computing third derivatives,
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
half-cheetah, forward/backward half-cheetah, goal velocity average a 0 2 0 1 2 number of gradient steps number of gradient steps 1 2 number of gradient steps ant, goal velocity ant, forward/backward MAML (ours) pretrained random â*> oracle 1 2 3 number of gradient steps
half-cheetah, forward/backward a 0 2 1 2 number of gradient steps
2 0 1 2 number of gradient steps ant, goal velocity
ant, forward/backward MAML (ours) pretrained random â*> oracle 1 2 3 number of gradient steps | 1703.03400#43 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
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]
|
1703.03400 | 46 | Figure 5. Reinforcement learning results for the half-cheetah and ant locomotion tasks, with the tasks shown on the far right. Each gradient step requires additional samples from the environment, unlike the supervised learning tasks. The results show that MAML can adapt to new goal velocities and directions substantially faster than conventional pretraining or random initialization, achieving good performs in just two or three gradient steps. We exclude the goal velocity, random baseline curves, since the returns are much worse (< â200 for cheetah and < â25 for ant). | 1703.03400#46 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
"id": "1612.00796"
},
{
"id": "1611.02779"
},
{
"id": "1603.04467"
},
{
"id": "1703.05175"
},
{
"id": "1508.03854"
},
{
"id": "1611.05763"
}
]
|
1703.03400 | 47 | we use ï¬nite differences to compute the Hessian-vector products for TRPO. For both learning and meta-learning updates, we use the standard linear feature baseline pro- posed by Duan et al. (2016a), which is ï¬tted separately at each iteration for each sampled task in the batch. We com- pare to three baseline models: (a) pretraining one policy on all of the tasks and then ï¬ne-tuning, (b) training a policy from randomly initialized weights, and (c) an oracle policy which receives the parameters of the task as input, which for the tasks below corresponds to a goal position, goal di- rection, or goal velocity for the agent. The baseline models of (a) and (b) are ï¬ne-tuned with gradient descent with a manually tuned step size. Videos of the learned policies can be viewed at sites.google.com/view/maml 2D Navigation. In our ï¬rst meta-RL experiment, we study a set of tasks where a point agent must move to different goal positions in 2D, randomly chosen for each task within a unit square. The observation is the current 2D position, and actions correspond to velocity commands | 1703.03400#47 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
"id": "1612.00796"
},
{
"id": "1611.02779"
},
{
"id": "1603.04467"
},
{
"id": "1703.05175"
},
{
"id": "1508.03854"
},
{
"id": "1611.05763"
}
]
|
1703.03400 | 48 | different goal positions in 2D, randomly chosen for each task within a unit square. The observation is the current 2D position, and actions correspond to velocity commands clipped to be in the range [â0.1, 0.1]. The reward is the negative squared distance to the goal, and episodes terminate when the agent is within 0.01 of the goal or at the horizon of H = 100. The policy was trained with MAML to maximize performance after 1 policy gradient update using 20 trajectories. Ad- ditional hyperparameter settings for this problem and the following RL problems are in Appendix A.2. In our evalu- ation, we compare adaptation to a new task with up to 4 gra- dient updates, each with 40 samples. The results in Figure 4 show the adaptation performance of models that are initial- ized with MAML, conventional pretraining on the same set of tasks, random initialization, and an oracle policy that receives the goal position as input. The results show that MAML can learn a model that adapts much more quickly in a single gradient update, and furthermore continues to improve with additional updates. | 1703.03400#48 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | We propose an algorithm for meta-learning that is model-agnostic, in the
sense that it is compatible with any model trained with gradient descent and
applicable to a variety of different learning problems, including
classification, regression, and reinforcement learning. The goal of
meta-learning is to train a model on a variety of learning tasks, such that it
can solve new learning tasks using only a small number of training samples. In
our approach, the parameters of the model are explicitly trained such that a
small number of gradient steps with a small amount of training data from a new
task will produce good generalization performance on that task. In effect, our
method trains the model to be easy to fine-tune. We demonstrate that this
approach leads to state-of-the-art performance on two few-shot image
classification benchmarks, produces good results on few-shot regression, and
accelerates fine-tuning for policy gradient reinforcement learning with neural
network policies. | http://arxiv.org/pdf/1703.03400 | Chelsea Finn, Pieter Abbeel, Sergey Levine | cs.LG, cs.AI, cs.CV, cs.NE | ICML 2017. Code at https://github.com/cbfinn/maml, Videos of RL
results at https://sites.google.com/view/maml, Blog post at
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ | null | cs.LG | 20170309 | 20170718 | [
{
"id": "1612.00796"
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{
"id": "1611.02779"
},
{
"id": "1603.04467"
},
{
"id": "1703.05175"
},
{
"id": "1508.03854"
},
{
"id": "1611.05763"
}
]
|
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