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Statistical inference for individual fairness | 19 | iclr | 0 | 0 | 2023-06-18 09:25:11.648000 | https://github.com/smaityumich/individual-fairness-testing | 3 | Statistical inference for individual fairness | https://scholar.google.com/scholar?cluster=6638472826434379762&hl=en&as_sdt=0,5 | 2 | 2,021 |
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning | 83 | iclr | 20 | 10 | 2023-06-18 09:25:11.857000 | https://github.com/alfworld/alfworld | 99 | Alfworld: Aligning text and embodied environments for interactive learning | https://scholar.google.com/scholar?cluster=11544973336902610716&hl=en&as_sdt=0,33 | 6 | 2,021 |
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification | 64 | iclr | 1 | 1 | 2023-06-18 09:25:12.061000 | https://github.com/utrerf/robust_transfer_learning | 11 | Adversarially-trained deep nets transfer better: Illustration on image classification | https://scholar.google.com/scholar?cluster=2203642732634467996&hl=en&as_sdt=0,41 | 6 | 2,021 |
Calibration of Neural Networks using Splines | 65 | iclr | 1 | 1 | 2023-06-18 09:25:12.264000 | https://github.com/kartikgupta-at-anu/spline-calibration | 15 | Calibration of neural networks using splines | https://scholar.google.com/scholar?cluster=16036759734574308055&hl=en&as_sdt=0,5 | 2 | 2,021 |
Rethinking Positional Encoding in Language Pre-training | 152 | iclr | 26 | 11 | 2023-06-18 09:25:12.467000 | https://github.com/guolinke/TUPE | 238 | Rethinking positional encoding in language pre-training | https://scholar.google.com/scholar?cluster=13553136852407909165&hl=en&as_sdt=0,33 | 6 | 2,021 |
Discovering Non-monotonic Autoregressive Orderings with Variational Inference | 3 | iclr | 3 | 0 | 2023-06-18 09:25:12.671000 | https://github.com/xuanlinli17/autoregressive_inference | 10 | Discovering non-monotonic autoregressive orderings with variational inference | https://scholar.google.com/scholar?cluster=14307542819344534269&hl=en&as_sdt=0,5 | 2 | 2,021 |
Differentiable Trust Region Layers for Deep Reinforcement Learning | 12 | iclr | 3 | 0 | 2023-06-18 09:25:12.874000 | https://github.com/boschresearch/trust-region-layers | 9 | Differentiable trust region layers for deep reinforcement learning | https://scholar.google.com/scholar?cluster=5230487248575578545&hl=en&as_sdt=0,33 | 4 | 2,021 |
SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization | 85 | iclr | 11 | 3 | 2023-06-18 09:25:13.078000 | https://github.com/SaliencyMix/SaliencyMix | 29 | Saliencymix: A saliency guided data augmentation strategy for better regularization | https://scholar.google.com/scholar?cluster=13015633056720744259&hl=en&as_sdt=0,47 | 2 | 2,021 |
Task-Agnostic Morphology Evolution | 12 | iclr | 4 | 0 | 2023-06-18 09:25:13.281000 | https://github.com/jhejna/morphology-opt | 18 | Task-agnostic morphology evolution | https://scholar.google.com/scholar?cluster=14695430945522716780&hl=en&as_sdt=0,24 | 2 | 2,021 |
Learning Associative Inference Using Fast Weight Memory | 30 | iclr | 5 | 1 | 2023-06-18 09:25:13.484000 | https://github.com/ischlag/Fast-Weight-Memory-public | 22 | Learning associative inference using fast weight memory | https://scholar.google.com/scholar?cluster=16934053175834221248&hl=en&as_sdt=0,33 | 2 | 2,021 |
Boost then Convolve: Gradient Boosting Meets Graph Neural Networks | 30 | iclr | 32 | 4 | 2023-06-18 09:25:13.688000 | https://github.com/nd7141/bgnn | 147 | Boost then convolve: Gradient boosting meets graph neural networks | https://scholar.google.com/scholar?cluster=10385206345451191815&hl=en&as_sdt=0,33 | 7 | 2,021 |
Network Pruning That Matters: A Case Study on Retraining Variants | 33 | iclr | 0 | 0 | 2023-06-18 09:25:13.892000 | https://github.com/lehduong/NPTM | 18 | Network pruning that matters: A case study on retraining variants | https://scholar.google.com/scholar?cluster=11116406662697084057&hl=en&as_sdt=0,22 | 2 | 2,021 |
Differentiable Segmentation of Sequences | 1 | iclr | 2 | 0 | 2023-06-18 09:25:14.094000 | https://github.com/diozaka/diffseg | 4 | Differentiable Segmentation of Sequences | https://scholar.google.com/scholar?cluster=460118456936482519&hl=en&as_sdt=0,5 | 1 | 2,021 |
Learning Deep Features in Instrumental Variable Regression | 41 | iclr | 5 | 0 | 2023-06-18 09:25:14.298000 | https://github.com/liyuan9988/DeepFeatureIV | 11 | Learning deep features in instrumental variable regression | https://scholar.google.com/scholar?cluster=17960670738858141531&hl=en&as_sdt=0,31 | 1 | 2,021 |
Graph Information Bottleneck for Subgraph Recognition | 65 | iclr | 4 | 0 | 2023-06-18 09:25:14.501000 | https://github.com/Samyu0304/graph-information-bottleneck-for-Subgraph-Recognition | 30 | Graph information bottleneck for subgraph recognition | https://scholar.google.com/scholar?cluster=12146903332537302158&hl=en&as_sdt=0,5 | 2 | 2,021 |
In Search of Lost Domain Generalization | 623 | iclr | 250 | 4 | 2023-06-18 09:25:14.705000 | https://github.com/facebookresearch/DomainBed | 1,087 | In search of lost domain generalization | https://scholar.google.com/scholar?cluster=5341652609507299465&hl=en&as_sdt=0,5 | 34 | 2,021 |
CoCon: A Self-Supervised Approach for Controlled Text Generation | 56 | iclr | 22 | 5 | 2023-06-18 09:25:14.908000 | https://github.com/alvinchangw/COCON_ICLR2021 | 87 | Cocon: A self-supervised approach for controlled text generation | https://scholar.google.com/scholar?cluster=5100156024568984026&hl=en&as_sdt=0,47 | 5 | 2,021 |
CT-Net: Channel Tensorization Network for Video Classification | 31 | iclr | 11 | 0 | 2023-06-18 09:25:15.111000 | https://github.com/Andy1621/CT-Net | 34 | Ct-net: Channel tensorization network for video classification | https://scholar.google.com/scholar?cluster=14793670932380609397&hl=en&as_sdt=0,5 | 2 | 2,021 |
Symmetry-Aware Actor-Critic for 3D Molecular Design | 47 | iclr | 22 | 7 | 2023-06-18 09:25:15.315000 | https://github.com/gncs/molgym | 94 | Symmetry-aware actor-critic for 3d molecular design | https://scholar.google.com/scholar?cluster=6834309222206333717&hl=en&as_sdt=0,5 | 5 | 2,021 |
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation | 174 | iclr | 22 | 9 | 2023-06-18 09:25:15.518000 | https://github.com/googleinterns/wss | 149 | Pseudoseg: Designing pseudo labels for semantic segmentation | https://scholar.google.com/scholar?cluster=11801417491488488735&hl=en&as_sdt=0,5 | 10 | 2,021 |
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks | 9 | iclr | 0 | 0 | 2023-06-18 09:25:15.722000 | https://github.com/sgk98/CRM-Better-Mistakes | 7 | No cost likelihood manipulation at test time for making better mistakes in deep networks | https://scholar.google.com/scholar?cluster=7455201941557048589&hl=en&as_sdt=0,5 | 6 | 2,021 |
Distance-Based Regularisation of Deep Networks for Fine-Tuning | 24 | iclr | 3 | 1 | 2023-06-18 09:25:15.926000 | https://github.com/henrygouk/mars-finetuning | 17 | Distance-based regularisation of deep networks for fine-tuning | https://scholar.google.com/scholar?cluster=16025867309498919322&hl=en&as_sdt=0,14 | 3 | 2,021 |
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis | 134 | iclr | 90 | 14 | 2023-06-18 09:25:16.129000 | https://github.com/odegeasslbc/FastGAN-pytorch | 501 | Towards faster and stabilized gan training for high-fidelity few-shot image synthesis | https://scholar.google.com/scholar?cluster=1230561477008611475&hl=en&as_sdt=0,5 | 10 | 2,021 |
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network | 53 | iclr | 8 | 2 | 2023-06-18 09:25:16.332000 | https://github.com/chrundle/biprop | 38 | Multi-prize lottery ticket hypothesis: Finding accurate binary neural networks by pruning a randomly weighted network | https://scholar.google.com/scholar?cluster=10684547264347032569&hl=en&as_sdt=0,5 | 6 | 2,021 |
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization | 44 | iclr | 6 | 3 | 2023-06-18 09:25:16.536000 | https://github.com/yanghr/BSQ | 29 | BSQ: Exploring bit-level sparsity for mixed-precision neural network quantization | https://scholar.google.com/scholar?cluster=11996673667923018710&hl=en&as_sdt=0,44 | 2 | 2,021 |
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly | 11 | iclr | 8 | 1 | 2023-06-18 09:25:16.740000 | https://github.com/YuchenJin/autolrs | 38 | Autolrs: Automatic learning-rate schedule by bayesian optimization on the fly | https://scholar.google.com/scholar?cluster=8044903521552367518&hl=en&as_sdt=0,5 | 3 | 2,021 |
BERTology Meets Biology: Interpreting Attention in Protein Language Models | 181 | iclr | 46 | 3 | 2023-06-18 09:25:16.944000 | https://github.com/salesforce/provis | 273 | BERTology meets biology: interpreting attention in protein language models | https://scholar.google.com/scholar?cluster=2514418869300543698&hl=en&as_sdt=0,5 | 18 | 2,021 |
Learning Task-General Representations with Generative Neuro-Symbolic Modeling | 11 | iclr | 7 | 1 | 2023-06-18 09:25:17.147000 | https://github.com/rfeinman/GNS-Modeling | 22 | Learning task-general representations with generative neuro-symbolic modeling | https://scholar.google.com/scholar?cluster=1335404082385789329&hl=en&as_sdt=0,33 | 5 | 2,021 |
Training independent subnetworks for robust prediction | 127 | iclr | 178 | 119 | 2023-06-18 09:25:17.350000 | https://github.com/google/uncertainty-baselines | 1,244 | Training independent subnetworks for robust prediction | https://scholar.google.com/scholar?cluster=9264084238315698016&hl=en&as_sdt=0,43 | 20 | 2,021 |
Meta-Learning of Structured Task Distributions in Humans and Machines | 7 | iclr | 2 | 0 | 2023-06-18 09:25:17.554000 | https://github.com/sreejank/Compositional_MetaRL | 6 | Meta-learning of structured task distributions in humans and machines | https://scholar.google.com/scholar?cluster=10148595521419901644&hl=en&as_sdt=0,34 | 2 | 2,021 |
BiPointNet: Binary Neural Network for Point Clouds | 33 | iclr | 12 | 4 | 2023-06-18 09:25:17.769000 | https://github.com/htqin/BiPointNet | 66 | Bipointnet: Binary neural network for point clouds | https://scholar.google.com/scholar?cluster=2821902497514525897&hl=en&as_sdt=0,5 | 5 | 2,021 |
Benchmarks for Deep Off-Policy Evaluation | 47 | iclr | 11 | 2 | 2023-06-18 09:25:17.973000 | https://github.com/google-research/deep_ope | 78 | Benchmarks for deep off-policy evaluation | https://scholar.google.com/scholar?cluster=4005543467911115320&hl=en&as_sdt=0,19 | 8 | 2,021 |
NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation | 25 | iclr | 11 | 0 | 2023-06-18 09:25:18.177000 | https://github.com/Angtian/NeMo | 78 | Nemo: Neural mesh models of contrastive features for robust 3d pose estimation | https://scholar.google.com/scholar?cluster=10173086417139954179&hl=en&as_sdt=0,5 | 6 | 2,021 |
On Graph Neural Networks versus Graph-Augmented MLPs | 30 | iclr | 0 | 0 | 2023-06-18 09:25:18.381000 | https://github.com/leichen2018/GNN_vs_GAMLP | 5 | On graph neural networks versus graph-augmented mlps | https://scholar.google.com/scholar?cluster=13883666734002011064&hl=en&as_sdt=0,23 | 2 | 2,021 |
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization | 98 | iclr | 4 | 2 | 2023-06-18 09:25:18.584000 | https://github.com/matsuolab/BREMEN | 49 | Deployment-efficient reinforcement learning via model-based offline optimization | https://scholar.google.com/scholar?cluster=6135669671400204615&hl=en&as_sdt=0,5 | 14 | 2,021 |
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs | 81 | iclr | 26 | 15 | 2023-06-18 09:25:18.788000 | https://github.com/DeepGraphLearning/RNNLogic | 105 | Rnnlogic: Learning logic rules for reasoning on knowledge graphs | https://scholar.google.com/scholar?cluster=15092610783958587096&hl=en&as_sdt=0,33 | 6 | 2,021 |
WaNet - Imperceptible Warping-based Backdoor Attack | 199 | iclr | 16 | 2 | 2023-06-18 09:25:18.992000 | https://github.com/VinAIResearch/Warping-based_Backdoor_Attack-release | 73 | Wanet--imperceptible warping-based backdoor attack | https://scholar.google.com/scholar?cluster=704714382831762036&hl=en&as_sdt=0,33 | 5 | 2,021 |
Prototypical Contrastive Learning of Unsupervised Representations | 594 | iclr | 75 | 5 | 2023-06-18 09:25:19.195000 | https://github.com/salesforce/PCL | 476 | Prototypical contrastive learning of unsupervised representations | https://scholar.google.com/scholar?cluster=298080063887760247&hl=en&as_sdt=0,31 | 16 | 2,021 |
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling | 61 | iclr | 5 | 0 | 2023-06-18 09:25:19.398000 | https://github.com/benbo/interactive-weak-supervision | 28 | Interactive weak supervision: Learning useful heuristics for data labeling | https://scholar.google.com/scholar?cluster=15628651718896902730&hl=en&as_sdt=0,31 | 3 | 2,021 |
Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation | 110 | iclr | 4 | 0 | 2023-06-18 09:25:19.602000 | https://github.com/jungokasai/deep-shallow | 39 | Deep encoder, shallow decoder: Reevaluating non-autoregressive machine translation | https://scholar.google.com/scholar?cluster=9322073775736159949&hl=en&as_sdt=0,7 | 3 | 2,021 |
PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences | 67 | iclr | 11 | 1 | 2023-06-18 09:25:19.804000 | https://github.com/hehefan/Point-Spatio-Temporal-Convolution | 76 | Pstnet: Point spatio-temporal convolution on point cloud sequences | https://scholar.google.com/scholar?cluster=2334272316624788650&hl=en&as_sdt=0,29 | 2 | 2,021 |
Prototypical Representation Learning for Relation Extraction | 33 | iclr | 5 | 6 | 2023-06-18 09:25:20.007000 | https://github.com/Alibaba-NLP/ProtoRE | 30 | Prototypical representation learning for relation extraction | https://scholar.google.com/scholar?cluster=12544006759905435219&hl=en&as_sdt=0,5 | 6 | 2,021 |
Layer-adaptive Sparsity for the Magnitude-based Pruning | 62 | iclr | 5 | 2 | 2023-06-18 09:25:20.211000 | https://github.com/jaeho-lee/layer-adaptive-sparsity | 40 | Layer-adaptive sparsity for the magnitude-based pruning | https://scholar.google.com/scholar?cluster=16870181998029600993&hl=en&as_sdt=0,33 | 1 | 2,021 |
Refining Deep Generative Models via Discriminator Gradient Flow | 26 | iclr | 5 | 0 | 2023-06-18 09:25:20.414000 | https://github.com/clear-nus/DGflow | 15 | Refining deep generative models via discriminator gradient flow | https://scholar.google.com/scholar?cluster=6216370278663020566&hl=en&as_sdt=0,5 | 3 | 2,021 |
Lipschitz Recurrent Neural Networks | 65 | iclr | 6 | 0 | 2023-06-18 09:25:20.618000 | https://github.com/erichson/LipschitzRNN | 22 | Lipschitz recurrent neural networks | https://scholar.google.com/scholar?cluster=9494951983450732150&hl=en&as_sdt=0,5 | 4 | 2,021 |
Learning Hyperbolic Representations of Topological Features | 8 | iclr | 0 | 0 | 2023-06-18 09:25:20.835000 | https://github.com/pkyriakis/permanifold | 4 | Learning hyperbolic representations of topological features | https://scholar.google.com/scholar?cluster=6250242644104147473&hl=en&as_sdt=0,5 | 3 | 2,021 |
Risk-Averse Offline Reinforcement Learning | 52 | iclr | 3 | 1 | 2023-06-18 09:25:21.039000 | https://github.com/nuria95/O-RAAC | 31 | Risk-averse offline reinforcement learning | https://scholar.google.com/scholar?cluster=13690519039445695672&hl=en&as_sdt=0,5 | 2 | 2,021 |
Group Equivariant Stand-Alone Self-Attention For Vision | 38 | iclr | 4 | 2 | 2023-06-18 09:25:21.242000 | https://github.com/dwromero/g_selfatt | 25 | Group equivariant stand-alone self-attention for vision | https://scholar.google.com/scholar?cluster=6833601088061308138&hl=en&as_sdt=0,10 | 2 | 2,021 |
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning | 39 | iclr | 5 | 1 | 2023-06-18 09:25:21.446000 | https://github.com/SamuelHorvath/Compressed_SGD_PyTorch | 11 | A better alternative to error feedback for communication-efficient distributed learning | https://scholar.google.com/scholar?cluster=3097136742513033323&hl=en&as_sdt=0,5 | 3 | 2,021 |
Capturing Label Characteristics in VAEs | 24 | iclr | 4 | 2 | 2023-06-18 09:25:21.650000 | https://github.com/thwjoy/ccvae | 10 | Capturing label characteristics in VAEs | https://scholar.google.com/scholar?cluster=9136485523673709879&hl=en&as_sdt=0,33 | 3 | 2,021 |
InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective | 71 | iclr | 4 | 0 | 2023-06-18 09:25:21.852000 | https://github.com/AI-secure/InfoBERT | 75 | Infobert: Improving robustness of language models from an information theoretic perspective | https://scholar.google.com/scholar?cluster=12094007183330442951&hl=en&as_sdt=0,14 | 3 | 2,021 |
DrNAS: Dirichlet Neural Architecture Search | 83 | iclr | 13 | 2 | 2023-06-18 09:25:22.056000 | https://github.com/xiangning-chen/DrNAS | 39 | Drnas: Dirichlet neural architecture search | https://scholar.google.com/scholar?cluster=10097373512584874749&hl=en&as_sdt=0,5 | 3 | 2,021 |
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration | 10 | iclr | 4 | 0 | 2023-06-18 09:25:22.260000 | https://github.com/jaekyeom/drop-bottleneck | 11 | Drop-bottleneck: Learning discrete compressed representation for noise-robust exploration | https://scholar.google.com/scholar?cluster=4970327572686173895&hl=en&as_sdt=0,5 | 1 | 2,021 |
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning | 25 | iclr | 1 | 1 | 2023-06-18 09:25:22.464000 | https://github.com/SunbowLiu/SurfaceFusion | 23 | Understanding and improving encoder layer fusion in sequence-to-sequence learning | https://scholar.google.com/scholar?cluster=14614453829953722728&hl=en&as_sdt=0,11 | 4 | 2,021 |
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling | 6 | iclr | 0 | 0 | 2023-06-18 09:25:22.667000 | https://github.com/djordjemila/sdn | 34 | Spatial dependency networks: Neural layers for improved generative image modeling | https://scholar.google.com/scholar?cluster=4211572628480421542&hl=en&as_sdt=0,6 | 4 | 2,021 |
Revisiting Locally Supervised Learning: an Alternative to End-to-end Training | 45 | iclr | 18 | 3 | 2023-06-18 09:25:22.870000 | https://github.com/blackfeather-wang/InfoPro-Pytorch | 85 | Revisiting locally supervised learning: an alternative to end-to-end training | https://scholar.google.com/scholar?cluster=9055003625249096504&hl=en&as_sdt=0,14 | 4 | 2,021 |
Gradient Origin Networks | 11 | iclr | 18 | 2 | 2023-06-18 09:25:23.073000 | https://github.com/cwkx/GON | 157 | Gradient origin networks | https://scholar.google.com/scholar?cluster=861384408190875414&hl=en&as_sdt=0,5 | 11 | 2,021 |
Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets | 22 | iclr | 8 | 3 | 2023-06-18 09:25:23.276000 | https://github.com/HayeonLee/MetaD2A | 53 | Rapid neural architecture search by learning to generate graphs from datasets | https://scholar.google.com/scholar?cluster=7579199201764554515&hl=en&as_sdt=0,5 | 4 | 2,021 |
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization | 14 | iclr | 2 | 0 | 2023-06-18 09:25:23.481000 | https://github.com/bethgelab/testing_visualizations | 10 | Exemplary natural images explain CNN activations better than state-of-the-art feature visualization | https://scholar.google.com/scholar?cluster=4262811630228932097&hl=en&as_sdt=0,44 | 10 | 2,021 |
Adversarial score matching and improved sampling for image generation | 62 | iclr | 19 | 0 | 2023-06-18 09:25:23.683000 | https://github.com/AlexiaJM/AdversarialConsistentScoreMatching | 116 | Adversarial score matching and improved sampling for image generation | https://scholar.google.com/scholar?cluster=10784754295814543422&hl=en&as_sdt=0,44 | 6 | 2,021 |
Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective | 97 | iclr | 3 | 0 | 2023-06-18 09:25:23.886000 | https://github.com/balcilar/gnn-spectral-expressive-power | 39 | Analyzing the expressive power of graph neural networks in a spectral perspective | https://scholar.google.com/scholar?cluster=12539425234528098281&hl=en&as_sdt=0,5 | 1 | 2,021 |
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients | 226 | iclr | 26 | 1 | 2023-06-18 09:25:24.089000 | https://github.com/dem123456789/HeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients | 89 | HeteroFL: Computation and communication efficient federated learning for heterogeneous clients | https://scholar.google.com/scholar?cluster=2499958009868244362&hl=en&as_sdt=0,22 | 2 | 2,021 |
DINO: A Conditional Energy-Based GAN for Domain Translation | 4 | iclr | 2 | 0 | 2023-06-18 09:25:24.294000 | https://github.com/DinoMan/DINO | 16 | Dino: A conditional energy-based gan for domain translation | https://scholar.google.com/scholar?cluster=16181191897980218531&hl=en&as_sdt=0,5 | 4 | 2,021 |
Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning | 45 | iclr | 25 | 4 | 2023-06-18 09:25:24.497000 | https://github.com/twke18/SPML | 92 | Universal weakly supervised segmentation by pixel-to-segment contrastive learning | https://scholar.google.com/scholar?cluster=2575509645382870246&hl=en&as_sdt=0,43 | 5 | 2,021 |
C-Learning: Horizon-Aware Cumulative Accessibility Estimation | 1 | iclr | 3 | 0 | 2023-06-18 09:25:24.701000 | https://github.com/layer6ai-labs/CAE | 3 | C-learning: Horizon-aware cumulative accessibility estimation | https://scholar.google.com/scholar?cluster=1403006878446325518&hl=en&as_sdt=0,44 | 6 | 2,021 |
Neurally Augmented ALISTA | 11 | iclr | 5 | 0 | 2023-06-18 09:25:24.905000 | https://github.com/feeds/na-alista | 13 | Neurally augmented ALISTA | https://scholar.google.com/scholar?cluster=3734900423445750140&hl=en&as_sdt=0,36 | 5 | 2,021 |
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization | 60 | iclr | 16 | 1 | 2023-06-18 09:25:25.108000 | https://github.com/CW-Huang/CP-Flow | 73 | Convex potential flows: Universal probability distributions with optimal transport and convex optimization | https://scholar.google.com/scholar?cluster=10968638702827610347&hl=en&as_sdt=0,36 | 5 | 2,021 |
Wasserstein Embedding for Graph Learning | 46 | iclr | 2 | 1 | 2023-06-18 09:25:25.312000 | https://github.com/navid-naderi/WEGL | 25 | Wasserstein embedding for graph learning | https://scholar.google.com/scholar?cluster=318944885595116091&hl=en&as_sdt=0,5 | 3 | 2,021 |
Grounding Language to Autonomously-Acquired Skills via Goal Generation | 42 | iclr | 3 | 0 | 2023-06-18 09:25:25.517000 | https://github.com/akakzia/decstr | 15 | Grounding language to autonomously-acquired skills via goal generation | https://scholar.google.com/scholar?cluster=192435658949853668&hl=en&as_sdt=0,34 | 2 | 2,021 |
Isometric Transformation Invariant and Equivariant Graph Convolutional Networks | 21 | iclr | 6 | 0 | 2023-06-18 09:25:25.721000 | https://github.com/yellowshippo/isogcn-iclr2021 | 41 | Isometric transformation invariant and equivariant graph convolutional networks | https://scholar.google.com/scholar?cluster=8837825832802039712&hl=en&as_sdt=0,1 | 1 | 2,021 |
R-GAP: Recursive Gradient Attack on Privacy | 57 | iclr | 1 | 0 | 2023-06-18 09:25:25.934000 | https://github.com/JunyiZhu-AI/R-GAP | 28 | R-gap: Recursive gradient attack on privacy | https://scholar.google.com/scholar?cluster=15519567665502998239&hl=en&as_sdt=0,5 | 2 | 2,021 |
Multiplicative Filter Networks | 60 | iclr | 7 | 1 | 2023-06-18 09:25:26.138000 | https://github.com/boschresearch/multiplicative-filter-networks | 72 | Multiplicative filter networks | https://scholar.google.com/scholar?cluster=2058143723489535198&hl=en&as_sdt=0,47 | 8 | 2,021 |
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks | 45 | iclr | 8 | 2 | 2023-06-18 09:25:26.341000 | https://github.com/RobertCsordas/modules | 33 | Are neural nets modular? inspecting functional modularity through differentiable weight masks | https://scholar.google.com/scholar?cluster=5376725240371408845&hl=en&as_sdt=0,5 | 0 | 2,021 |
Modeling the Second Player in Distributionally Robust Optimization | 17 | iclr | 7 | 0 | 2023-06-18 09:25:26.546000 | https://github.com/pmichel31415/P-DRO | 18 | Modeling the second player in distributionally robust optimization | https://scholar.google.com/scholar?cluster=16015230267051780457&hl=en&as_sdt=0,33 | 2 | 2,021 |
Private Post-GAN Boosting | 21 | iclr | 4 | 6 | 2023-06-18 09:25:26.750000 | https://github.com/mneunhoe/post-gan-boosting | 9 | Private post-GAN boosting | https://scholar.google.com/scholar?cluster=937740189813979153&hl=en&as_sdt=0,33 | 4 | 2,021 |
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis | 126 | iclr | 171 | 73 | 2023-06-18 09:25:26.953000 | https://github.com/NVIDIA/flowtron | 839 | Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis | https://scholar.google.com/scholar?cluster=1579689582070242490&hl=en&as_sdt=0,40 | 31 | 2,021 |
Learning Structural Edits via Incremental Tree Transformations | 20 | iclr | 3 | 0 | 2023-06-18 09:25:27.157000 | https://github.com/neulab/incremental_tree_edit | 40 | Learning structural edits via incremental tree transformations | https://scholar.google.com/scholar?cluster=785545051863300366&hl=en&as_sdt=0,1 | 13 | 2,021 |
Sample-Efficient Automated Deep Reinforcement Learning | 26 | iclr | 6 | 0 | 2023-06-18 09:25:27.361000 | https://github.com/automl/SEARL | 32 | Sample-efficient automated deep reinforcement learning | https://scholar.google.com/scholar?cluster=1828733930772382760&hl=en&as_sdt=0,33 | 10 | 2,021 |
Multiscale Score Matching for Out-of-Distribution Detection | 18 | iclr | 0 | 1 | 2023-06-18 09:25:27.565000 | https://github.com/ahsanMah/msma | 6 | Multiscale score matching for out-of-distribution detection | https://scholar.google.com/scholar?cluster=3312026787172969565&hl=en&as_sdt=0,5 | 3 | 2,021 |
Linear Last-iterate Convergence in Constrained Saddle-point Optimization | 66 | iclr | 1 | 0 | 2023-06-18 09:25:27.768000 | https://github.com/bahh723/OGDA-last-iterate | 1 | Linear last-iterate convergence in constrained saddle-point optimization | https://scholar.google.com/scholar?cluster=11705572357313467666&hl=en&as_sdt=0,5 | 4 | 2,021 |
Learning advanced mathematical computations from examples | 20 | iclr | 12 | 0 | 2023-06-18 09:25:27.972000 | https://github.com/facebookresearch/MathsFromExamples | 173 | Learning advanced mathematical computations from examples | https://scholar.google.com/scholar?cluster=8069536277199398832&hl=en&as_sdt=0,5 | 10 | 2,021 |
Generalized Energy Based Models | 83 | iclr | 4 | 1 | 2023-06-18 09:25:28.176000 | https://github.com/MichaelArbel/GeneralizedEBM | 46 | Generalized energy based models | https://scholar.google.com/scholar?cluster=8950051300346719301&hl=en&as_sdt=0,5 | 4 | 2,021 |
Beyond Categorical Label Representations for Image Classification | 2 | iclr | 8 | 1 | 2023-06-18 09:25:28.380000 | https://github.com/BoyuanChen/label_representations | 24 | Beyond Categorical Label Representations for Image Classification | https://scholar.google.com/scholar?cluster=6100870767960512656&hl=en&as_sdt=0,38 | 3 | 2,021 |
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers | 53 | iclr | 12 | 4 | 2023-06-18 09:25:28.583000 | https://github.com/salesforce/coco-dst | 52 | Coco: Controllable counterfactuals for evaluating dialogue state trackers | https://scholar.google.com/scholar?cluster=2147186287214525366&hl=en&as_sdt=0,38 | 5 | 2,021 |
Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models | 22 | iclr | 6 | 0 | 2023-06-18 09:25:28.787000 | https://github.com/point0bar1/ebm-defense | 17 | Stochastic security: Adversarial defense using long-run dynamics of energy-based models | https://scholar.google.com/scholar?cluster=1702716547695193492&hl=en&as_sdt=0,5 | 2 | 2,021 |
PDE-Driven Spatiotemporal Disentanglement | 18 | iclr | 3 | 0 | 2023-06-18 09:25:28.992000 | https://github.com/JeremDona/spatiotemporal_variable_separation | 25 | Pde-driven spatiotemporal disentanglement | https://scholar.google.com/scholar?cluster=11182191467887081005&hl=en&as_sdt=0,5 | 3 | 2,021 |
Directed Acyclic Graph Neural Networks | 57 | iclr | 20 | 1 | 2023-06-18 09:25:29.196000 | https://github.com/vthost/DAGNN | 80 | Directed acyclic graph neural networks | https://scholar.google.com/scholar?cluster=13529849835566425247&hl=en&as_sdt=0,33 | 3 | 2,021 |
QPLEX: Duplex Dueling Multi-Agent Q-Learning | 248 | iclr | 25 | 4 | 2023-06-18 09:25:29.400000 | https://github.com/wjh720/QPLEX | 71 | Qplex: Duplex dueling multi-agent q-learning | https://scholar.google.com/scholar?cluster=785256568815923824&hl=en&as_sdt=0,23 | 4 | 2,021 |
Learning Energy-Based Models by Diffusion Recovery Likelihood | 64 | iclr | 13 | 6 | 2023-06-18 09:25:29.604000 | https://github.com/ruiqigao/recovery_likelihood | 41 | Learning energy-based models by diffusion recovery likelihood | https://scholar.google.com/scholar?cluster=4399294843209736764&hl=en&as_sdt=0,5 | 4 | 2,021 |
Neural Networks for Learning Counterfactual G-Invariances from Single Environments | 4 | iclr | 0 | 0 | 2023-06-18 09:25:29.809000 | https://github.com/PurdueMINDS/NN_CGInvariance | 1 | Neural networks for learning counterfactual g-invariances from single environments | https://scholar.google.com/scholar?cluster=11398104939483895599&hl=en&as_sdt=0,5 | 5 | 2,021 |
On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections | 71 | iclr | 4 | 0 | 2023-06-18 09:25:30.025000 | https://github.com/brandeis-machine-learning/FairAdj | 7 | On dyadic fairness: Exploring and mitigating bias in graph connections | https://scholar.google.com/scholar?cluster=9084547275542590284&hl=en&as_sdt=0,5 | 2 | 2,021 |
Faster Binary Embeddings for Preserving Euclidean Distances | 2 | iclr | 0 | 0 | 2023-06-18 09:25:30.229000 | https://github.com/jayzhang0727/Faster-Binary-Embeddings-for-Preserving-Euclidean-Distances | 3 | Faster binary embeddings for preserving euclidean distances | https://scholar.google.com/scholar?cluster=16441241350533761738&hl=en&as_sdt=0,5 | 1 | 2,021 |
Learning and Evaluating Representations for Deep One-Class Classification | 134 | iclr | 28 | 3 | 2023-06-18 09:25:30.434000 | https://github.com/google-research/deep_representation_one_class | 141 | Learning and evaluating representations for deep one-class classification | https://scholar.google.com/scholar?cluster=6458276904017990971&hl=en&as_sdt=0,14 | 7 | 2,021 |
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning | 6 | iclr | 0 | 0 | 2023-06-18 09:25:30.644000 | https://github.com/NamyeongK/USA_UFGSM | 1 | Repurposing pretrained models for robust out-of-domain few-shot learning | https://scholar.google.com/scholar?cluster=14110551403229588194&hl=en&as_sdt=0,5 | 0 | 2,021 |
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning | 300 | iclr | 38 | 5 | 2023-06-18 09:25:30.871000 | https://github.com/nayeemrizve/ups | 199 | In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning | https://scholar.google.com/scholar?cluster=18358012281479028989&hl=en&as_sdt=0,44 | 2 | 2,021 |
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning | 13 | iclr | 1 | 1 | 2023-06-18 09:25:31.074000 | https://github.com/necla-ml/cater-h | 6 | Hopper: Multi-hop transformer for spatiotemporal reasoning | https://scholar.google.com/scholar?cluster=15937741305189053323&hl=en&as_sdt=0,14 | 6 | 2,021 |
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization | 2 | iclr | 0 | 1 | 2023-06-18 09:25:31.279000 | https://github.com/mederrata/spmf | 2 | Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization | https://scholar.google.com/scholar?cluster=17630346324232626458&hl=en&as_sdt=0,5 | 9 | 2,021 |
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology | 17 | iclr | 5 | 2 | 2023-06-18 09:25:31.483000 | https://github.com/stanfordmlgroup/disentanglement | 36 | Evaluating the disentanglement of deep generative models through manifold topology | https://scholar.google.com/scholar?cluster=8056390107725360961&hl=en&as_sdt=0,47 | 5 | 2,021 |
Decoupling Global and Local Representations via Invertible Generative Flows | 12 | iclr | 12 | 3 | 2023-06-18 09:25:31.687000 | https://github.com/XuezheMax/wolf | 81 | Decoupling global and local representations via invertible generative flows | https://scholar.google.com/scholar?cluster=17803747103962637793&hl=en&as_sdt=0,34 | 4 | 2,021 |
Pre-training Text-to-Text Transformers for Concept-centric Common Sense | 45 | iclr | 0 | 2 | 2023-06-18 09:25:31.890000 | https://github.com/INK-USC/CALM | 26 | Pre-training text-to-text transformers for concept-centric common sense | https://scholar.google.com/scholar?cluster=8101587242954788676&hl=en&as_sdt=0,33 | 5 | 2,021 |
Subsets and Splits