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Differentially Private Learning Needs Better Features (or Much More Data) | 144 | iclr | 15 | 0 | 2023-06-18 09:24:30.750000 | https://github.com/ftramer/Handcrafted-DP | 67 | Differentially private learning needs better features (or much more data) | https://scholar.google.com/scholar?cluster=17298633673163365273&hl=en&as_sdt=0,33 | 1 | 2,021 |
Unsupervised Object Keypoint Learning using Local Spatial Predictability | 21 | iclr | 2 | 1 | 2023-06-18 09:24:30.953000 | https://github.com/agopal42/permakey | 10 | Unsupervised object keypoint learning using local spatial predictability | https://scholar.google.com/scholar?cluster=2846223975982040461&hl=en&as_sdt=0,43 | 3 | 2,021 |
DeepAveragers: Offline Reinforcement Learning By Solving Derived Non-Parametric MDPs | 11 | iclr | 117 | 2 | 2023-06-18 09:24:31.156000 | https://github.com/maximecb/gym-miniworld | 610 | Deepaveragers: Offline reinforcement learning by solving derived non-parametric mdps | https://scholar.google.com/scholar?cluster=11392379415434294495&hl=en&as_sdt=0,33 | 18 | 2,021 |
Learning from Protein Structure with Geometric Vector Perceptrons | 141 | iclr | 35 | 8 | 2023-06-18 09:24:31.359000 | https://github.com/drorlab/gvp-pytorch | 167 | Learning from protein structure with geometric vector perceptrons | https://scholar.google.com/scholar?cluster=151372908751868472&hl=en&as_sdt=0,33 | 11 | 2,021 |
Undistillable: Making A Nasty Teacher That CANNOT teach students | 24 | iclr | 12 | 3 | 2023-06-18 09:24:31.562000 | https://github.com/VITA-Group/Nasty-Teacher | 77 | Undistillable: Making a nasty teacher that cannot teach students | https://scholar.google.com/scholar?cluster=3474115554286885687&hl=en&as_sdt=0,10 | 12 | 2,021 |
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows | 112 | iclr | 168 | 63 | 2023-06-18 09:24:31.765000 | https://github.com/zalandoresearch/pytorch-ts | 1,006 | Multivariate probabilistic time series forecasting via conditioned normalizing flows | https://scholar.google.com/scholar?cluster=1580250645511202930&hl=en&as_sdt=0,33 | 24 | 2,021 |
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels | 484 | iclr | 51 | 2 | 2023-06-18 09:24:31.968000 | https://github.com/denisyarats/drq | 376 | Image augmentation is all you need: Regularizing deep reinforcement learning from pixels | https://scholar.google.com/scholar?cluster=11402905305811900268&hl=en&as_sdt=0,33 | 13 | 2,021 |
A Gradient Flow Framework For Analyzing Network Pruning | 28 | iclr | 7 | 0 | 2023-06-18 09:24:32.171000 | https://github.com/EkdeepSLubana/flowandprune | 21 | A gradient flow framework for analyzing network pruning | https://scholar.google.com/scholar?cluster=82764651389872637&hl=en&as_sdt=0,5 | 2 | 2,021 |
The Intrinsic Dimension of Images and Its Impact on Learning | 100 | iclr | 5 | 1 | 2023-06-18 09:24:32.374000 | https://github.com/ppope/dimensions | 49 | The intrinsic dimension of images and its impact on learning | https://scholar.google.com/scholar?cluster=4972021380000634715&hl=en&as_sdt=0,44 | 6 | 2,021 |
Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy | 4 | iclr | 1 | 0 | 2023-06-18 09:24:32.578000 | https://github.com/TaikiMiyagawa/SPRT-TANDEM | 12 | Sequential density ratio estimation for simultaneous optimization of speed and accuracy | https://scholar.google.com/scholar?cluster=17595723028286278692&hl=en&as_sdt=0,10 | 4 | 2,021 |
A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference | 33 | iclr | 1 | 0 | 2023-06-18 09:24:32.781000 | https://github.com/sanghyun-hong/deepsloth | 13 | A panda? no, it's a sloth: Slowdown attacks on adaptive multi-exit neural network inference | https://scholar.google.com/scholar?cluster=7387967890679036055&hl=en&as_sdt=0,43 | 2 | 2,021 |
Orthogonalizing Convolutional Layers with the Cayley Transform | 62 | iclr | 7 | 0 | 2023-06-18 09:24:32.984000 | https://github.com/locuslab/orthogonal-convolutions | 36 | Orthogonalizing convolutional layers with the cayley transform | https://scholar.google.com/scholar?cluster=7972253340344904687&hl=en&as_sdt=0,33 | 3 | 2,021 |
Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning | 23 | iclr | 5 | 1 | 2023-06-18 09:24:33.187000 | https://github.com/db-Lee/Meta-GMVAE | 33 | Meta-gmvae: Mixture of gaussian vae for unsupervised meta-learning | https://scholar.google.com/scholar?cluster=2848780669531491814&hl=en&as_sdt=0,33 | 2 | 2,021 |
Retrieval-Augmented Generation for Code Summarization via Hybrid GNN | 78 | iclr | 1 | 1 | 2023-06-18 09:24:33.391000 | https://github.com/shangqing-liu/CCSD-benchmark-for-code-summarization | 17 | Retrieval-augmented generation for code summarization via hybrid gnn | https://scholar.google.com/scholar?cluster=1074914140927042539&hl=en&as_sdt=0,33 | 2 | 2,021 |
Self-supervised Visual Reinforcement Learning with Object-centric Representations | 18 | iclr | 3 | 0 | 2023-06-18 09:24:33.596000 | https://github.com/martius-lab/SMORL | 19 | Self-supervised visual reinforcement learning with object-centric representations | https://scholar.google.com/scholar?cluster=14115681907548561734&hl=en&as_sdt=0,19 | 4 | 2,021 |
Neural Topic Model via Optimal Transport | 26 | iclr | 5 | 0 | 2023-06-18 09:24:33.807000 | https://github.com/ethanhezhao/NeuralSinkhornTopicModel | 14 | Neural topic model via optimal transport | https://scholar.google.com/scholar?cluster=689828574745146932&hl=en&as_sdt=0,14 | 1 | 2,021 |
Memory Optimization for Deep Networks | 14 | iclr | 19 | 1 | 2023-06-18 09:24:34.023000 | https://github.com/utsaslab/MONeT | 166 | Memory optimization for deep networks | https://scholar.google.com/scholar?cluster=6587488061913328550&hl=en&as_sdt=0,5 | 10 | 2,021 |
Stabilized Medical Image Attacks | 19 | iclr | 1 | 3 | 2023-06-18 09:24:34.226000 | https://github.com/imogenqi/SMA | 5 | Stabilized medical image attacks | https://scholar.google.com/scholar?cluster=5943786222126044204&hl=en&as_sdt=0,5 | 1 | 2,021 |
Quantifying Differences in Reward Functions | 34 | iclr | 5 | 6 | 2023-06-18 09:24:34.431000 | https://github.com/HumanCompatibleAI/evaluating-rewards | 52 | Quantifying differences in reward functions | https://scholar.google.com/scholar?cluster=3868524216566349741&hl=en&as_sdt=0,33 | 10 | 2,021 |
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery | 77 | iclr | 1 | 0 | 2023-06-18 09:24:34.637000 | https://github.com/yutxie/mars | 2 | Mars: Markov molecular sampling for multi-objective drug discovery | https://scholar.google.com/scholar?cluster=3117547435494031636&hl=en&as_sdt=0,47 | 1 | 2,021 |
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs | 81 | iclr | 5 | 2 | 2023-06-18 09:24:34.866000 | https://github.com/qualcomm-ai-research/gauge-equivariant-mesh-cnn | 56 | Gauge equivariant mesh CNNs: Anisotropic convolutions on geometric graphs | https://scholar.google.com/scholar?cluster=17703338276692634777&hl=en&as_sdt=0,5 | 5 | 2,021 |
Revisiting Dynamic Convolution via Matrix Decomposition | 34 | iclr | 13 | 4 | 2023-06-18 09:24:35.070000 | https://github.com/liyunsheng13/dcd | 116 | Revisiting dynamic convolution via matrix decomposition | https://scholar.google.com/scholar?cluster=18300094964606568091&hl=en&as_sdt=0,7 | 5 | 2,021 |
Explainable Deep One-Class Classification | 138 | iclr | 58 | 10 | 2023-06-18 09:24:35.273000 | https://github.com/liznerski/fcdd | 198 | Explainable deep one-class classification | https://scholar.google.com/scholar?cluster=1382712243609022780&hl=en&as_sdt=0,31 | 10 | 2,021 |
Neural Pruning via Growing Regularization | 71 | iclr | 17 | 0 | 2023-06-18 09:24:35.476000 | https://github.com/mingsun-tse/regularization-pruning | 70 | Neural pruning via growing regularization | https://scholar.google.com/scholar?cluster=12329421876682813123&hl=en&as_sdt=0,6 | 5 | 2,021 |
Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition | 36 | iclr | 21 | 2 | 2023-06-18 09:24:35.680000 | https://github.com/LeePleased/NegSampling-NER | 130 | Empirical analysis of unlabeled entity problem in named entity recognition | https://scholar.google.com/scholar?cluster=2091894969577971912&hl=en&as_sdt=0,5 | 2 | 2,021 |
Nearest Neighbor Machine Translation | 160 | iclr | 41 | 4 | 2023-06-18 09:24:35.883000 | https://github.com/urvashik/knnlm | 253 | Nearest neighbor machine translation | https://scholar.google.com/scholar?cluster=6208883901750253359&hl=en&as_sdt=0,10 | 7 | 2,021 |
Wandering within a world: Online contextualized few-shot learning | 25 | iclr | 6 | 2 | 2023-06-18 09:24:36.087000 | https://github.com/renmengye/oc-fewshot-public | 21 | Wandering within a world: Online contextualized few-shot learning | https://scholar.google.com/scholar?cluster=17017727329271450811&hl=en&as_sdt=0,5 | 8 | 2,021 |
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models | 63 | iclr | 12 | 2 | 2023-06-18 09:24:36.289000 | https://github.com/datake/AdaGCN | 50 | Adagcn: Adaboosting graph convolutional networks into deep models | https://scholar.google.com/scholar?cluster=9537937835922263498&hl=en&as_sdt=0,3 | 4 | 2,021 |
Meta Back-Translation | 16 | iclr | 7,332 | 1,026 | 2023-06-18 09:24:36.493000 | https://github.com/google-research/google-research | 29,803 | Meta back-translation | https://scholar.google.com/scholar?cluster=8104983143273406902&hl=en&as_sdt=0,5 | 728 | 2,021 |
Viewmaker Networks: Learning Views for Unsupervised Representation Learning | 49 | iclr | 11 | 2 | 2023-06-18 09:24:36.695000 | https://github.com/alextamkin/viewmaker | 32 | Viewmaker networks: Learning views for unsupervised representation learning | https://scholar.google.com/scholar?cluster=5109645673103206177&hl=en&as_sdt=0,3 | 2 | 2,021 |
Negative Data Augmentation | 59 | iclr | 4 | 0 | 2023-06-18 09:24:36.898000 | https://github.com/ermongroup/NDA | 22 | Negative data augmentation | https://scholar.google.com/scholar?cluster=1155111694700482040&hl=en&as_sdt=0,5 | 8 | 2,021 |
Teaching with Commentaries | 21 | iclr | 1 | 2 | 2023-06-18 09:24:37.101000 | https://github.com/googleinterns/commentaries | 5 | Teaching with commentaries | https://scholar.google.com/scholar?cluster=12512263277235607257&hl=en&as_sdt=0,5 | 2 | 2,021 |
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines | 243 | iclr | 21 | 3 | 2023-06-18 09:24:37.305000 | https://github.com/uds-lsv/bert-stable-fine-tuning | 128 | On the stability of fine-tuning bert: Misconceptions, explanations, and strong baselines | https://scholar.google.com/scholar?cluster=5096550339009628342&hl=en&as_sdt=0,5 | 12 | 2,021 |
Variational Information Bottleneck for Effective Low-Resource Fine-Tuning | 34 | iclr | 4 | 1 | 2023-06-18 09:24:37.509000 | https://github.com/rabeehk/vibert | 25 | Variational information bottleneck for effective low-resource fine-tuning | https://scholar.google.com/scholar?cluster=8332334041068386059&hl=en&as_sdt=0,5 | 2 | 2,021 |
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching | 117 | iclr | 7 | 0 | 2023-06-18 09:24:37.712000 | https://github.com/JonasGeiping/poisoning-gradient-matching | 77 | Witches' brew: Industrial scale data poisoning via gradient matching | https://scholar.google.com/scholar?cluster=12446963321584021008&hl=en&as_sdt=0,5 | 2 | 2,021 |
Deberta: decoding-Enhanced Bert with Disentangled Attention | 1,009 | iclr | 188 | 56 | 2023-06-18 09:24:37.916000 | https://github.com/microsoft/DeBERTa | 1,587 | Deberta: Decoding-enhanced bert with disentangled attention | https://scholar.google.com/scholar?cluster=17165415294113919367&hl=en&as_sdt=0,46 | 44 | 2,021 |
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning | 13 | iclr | 2 | 0 | 2023-06-18 09:24:38.119000 | https://github.com/isi-usc-edu/gttf | 7 | Graph traversal with tensor functionals: A meta-algorithm for scalable learning | https://scholar.google.com/scholar?cluster=4421735277125867362&hl=en&as_sdt=0,5 | 4 | 2,021 |
Diverse Video Generation using a Gaussian Process Trigger | 5 | iclr | 6 | 1 | 2023-06-18 09:24:38.323000 | https://github.com/shgaurav1/DVG | 16 | Diverse video generation using a Gaussian process trigger | https://scholar.google.com/scholar?cluster=4423790628235777527&hl=en&as_sdt=0,34 | 4 | 2,021 |
Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU | 64 | iclr | 26 | 13 | 2023-06-18 09:24:38.527000 | https://github.com/patrick-kidger/signatory | 222 | Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU | https://scholar.google.com/scholar?cluster=17137105822248313945&hl=en&as_sdt=0,32 | 10 | 2,021 |
MoPro: Webly Supervised Learning with Momentum Prototypes | 63 | iclr | 8 | 0 | 2023-06-18 09:24:38.743000 | https://github.com/salesforce/MoPro | 79 | Mopro: Webly supervised learning with momentum prototypes | https://scholar.google.com/scholar?cluster=3510417880461380553&hl=en&as_sdt=0,5 | 9 | 2,021 |
A Universal Representation Transformer Layer for Few-Shot Image Classification | 93 | iclr | 18 | 3 | 2023-06-18 09:24:38.964000 | https://github.com/liulu112601/URT | 96 | A universal representation transformer layer for few-shot image classification | https://scholar.google.com/scholar?cluster=6018140255832554871&hl=en&as_sdt=0,22 | 4 | 2,021 |
Learning perturbation sets for robust machine learning | 61 | iclr | 9 | 0 | 2023-06-18 09:24:39.168000 | https://github.com/locuslab/perturbation_learning | 63 | Learning perturbation sets for robust machine learning | https://scholar.google.com/scholar?cluster=14923687105877479161&hl=en&as_sdt=0,33 | 10 | 2,021 |
CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks | 10 | iclr | 3 | 0 | 2023-06-18 09:24:39.370000 | https://github.com/jiaqima/CopulaGNN | 10 | Copulagnn: Towards integrating representational and correlational roles of graphs in graph neural networks | https://scholar.google.com/scholar?cluster=15600465450888406918&hl=en&as_sdt=0,33 | 4 | 2,021 |
On the Critical Role of Conventions in Adaptive Human-AI Collaboration | 24 | iclr | 4 | 0 | 2023-06-18 09:24:39.573000 | https://github.com/Stanford-ILIAD/Conventions-ModularPolicy | 11 | On the critical role of conventions in adaptive human-AI collaboration | https://scholar.google.com/scholar?cluster=11035601410057323120&hl=en&as_sdt=0,33 | 2 | 2,021 |
On the Bottleneck of Graph Neural Networks and its Practical Implications | 338 | iclr | 18 | 0 | 2023-06-18 09:24:39.776000 | https://github.com/tech-srl/bottleneck | 85 | On the bottleneck of graph neural networks and its practical implications | https://scholar.google.com/scholar?cluster=5884209795367025285&hl=en&as_sdt=0,33 | 6 | 2,021 |
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability | 110 | iclr | 13 | 1 | 2023-06-18 09:24:39.979000 | https://github.com/locuslab/edge-of-stability | 35 | Gradient descent on neural networks typically occurs at the edge of stability | https://scholar.google.com/scholar?cluster=1829576952258168273&hl=en&as_sdt=0,5 | 3 | 2,021 |
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers | 36 | iclr | 2 | 0 | 2023-06-18 09:24:40.182000 | https://github.com/preetum/deep-bootstrap-code | 3 | The deep bootstrap framework: Good online learners are good offline generalizers | https://scholar.google.com/scholar?cluster=6565841002314510004&hl=en&as_sdt=0,33 | 1 | 2,021 |
What Can You Learn From Your Muscles? Learning Visual Representation from Human Interactions | 1 | iclr | 5 | 0 | 2023-06-18 09:24:40.387000 | https://github.com/ehsanik/muscleTorch | 34 | What can you learn from your muscles? Learning visual representation from human interactions | https://scholar.google.com/scholar?cluster=550456704334967809&hl=en&as_sdt=0,33 | 4 | 2,021 |
EEC: Learning to Encode and Regenerate Images for Continual Learning | 33 | iclr | 2 | 1 | 2023-06-18 09:24:40.590000 | https://github.com/aliayub7/EEC | 6 | Eec: Learning to encode and regenerate images for continual learning | https://scholar.google.com/scholar?cluster=4496455065101916683&hl=en&as_sdt=0,22 | 1 | 2,021 |
MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space | 37 | iclr | 7 | 0 | 2023-06-18 09:24:40.813000 | https://github.com/jamestszhim/modals | 39 | Modals: Modality-agnostic automated data augmentation in the latent space | https://scholar.google.com/scholar?cluster=500252256958905673&hl=en&as_sdt=0,39 | 3 | 2,021 |
Learning the Pareto Front with Hypernetworks | 65 | iclr | 10 | 0 | 2023-06-18 09:24:41.016000 | https://github.com/AvivNavon/pareto-hypernetworks | 83 | Learning the pareto front with hypernetworks | https://scholar.google.com/scholar?cluster=13675122104724715473&hl=en&as_sdt=0,50 | 3 | 2,021 |
Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors | 28 | iclr | 15 | 2 | 2023-06-18 09:24:41.219000 | https://github.com/asharakeh/probdet | 54 | Estimating and evaluating regression predictive uncertainty in deep object detectors | https://scholar.google.com/scholar?cluster=3972283505781057189&hl=en&as_sdt=0,33 | 1 | 2,021 |
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction | 143 | iclr | 46 | 21 | 2023-06-18 09:24:41.423000 | https://github.com/yhhhli/BRECQ | 180 | Brecq: Pushing the limit of post-training quantization by block reconstruction | https://scholar.google.com/scholar?cluster=4375514065793876125&hl=en&as_sdt=0,31 | 6 | 2,021 |
GraphCodeBERT: Pre-training Code Representations with Data Flow | 358 | iclr | 346 | 41 | 2023-06-18 09:24:41.630000 | https://github.com/microsoft/CodeBERT | 1,451 | Graphcodebert: Pre-training code representations with data flow | https://scholar.google.com/scholar?cluster=12215762142211425404&hl=en&as_sdt=0,33 | 25 | 2,021 |
Improve Object Detection with Feature-based Knowledge Distillation: Towards Accurate and Efficient Detectors | 99 | iclr | 6 | 7 | 2023-06-18 09:24:41.834000 | https://github.com/ArchipLab-LinfengZhang/Object-Detection-Knowledge-Distillation-ICLR2021 | 49 | Improve object detection with feature-based knowledge distillation: Towards accurate and efficient detectors | https://scholar.google.com/scholar?cluster=4883781250295766379&hl=en&as_sdt=0,33 | 5 | 2,021 |
A Temporal Kernel Approach for Deep Learning with Continuous-time Information | 2 | iclr | 53 | 12 | 2023-06-18 09:24:42.037000 | https://github.com/StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs | 222 | A temporal kernel approach for deep learning with continuous-time information | https://scholar.google.com/scholar?cluster=2677250892342211490&hl=en&as_sdt=0,33 | 3 | 2,021 |
How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision | 159 | iclr | 24 | 2 | 2023-06-18 09:24:42.240000 | https://github.com/dongkwan-kim/SuperGAT | 135 | How to find your friendly neighborhood: Graph attention design with self-supervision | https://scholar.google.com/scholar?cluster=7594913044183235646&hl=en&as_sdt=0,33 | 4 | 2,021 |
Interpretable Models for Granger Causality Using Self-explaining Neural Networks | 11 | iclr | 14 | 0 | 2023-06-18 09:24:42.443000 | https://github.com/i6092467/GVAR | 34 | Learning interaction rules from multi-animal trajectories via augmented behavioral models | https://scholar.google.com/scholar?cluster=13190745890031985835&hl=en&as_sdt=0,47 | 1 | 2,021 |
Meta-learning Symmetries by Reparameterization | 55 | iclr | 6 | 1 | 2023-06-18 09:24:42.646000 | https://github.com/AllanYangZhou/metalearning-symmetries | 48 | Meta-learning symmetries by reparameterization | https://scholar.google.com/scholar?cluster=9023763137137918184&hl=en&as_sdt=0,33 | 12 | 2,021 |
Removing Undesirable Feature Contributions Using Out-of-Distribution Data | 17 | iclr | 2 | 0 | 2023-06-18 09:24:42.850000 | https://github.com/Saehyung-Lee/OAT | 8 | Removing undesirable feature contributions using out-of-distribution data | https://scholar.google.com/scholar?cluster=16828055548257424172&hl=en&as_sdt=0,47 | 1 | 2,021 |
On the Universality of the Double Descent Peak in Ridgeless Regression | 11 | iclr | 1 | 0 | 2023-06-18 09:24:43.052000 | https://github.com/dholzmueller/universal_double_descent | 1 | On the universality of the double descent peak in ridgeless regression | https://scholar.google.com/scholar?cluster=6446983561543714244&hl=en&as_sdt=0,36 | 1 | 2,021 |
Fair Mixup: Fairness via Interpolation | 78 | iclr | 4 | 0 | 2023-06-18 09:24:43.255000 | https://github.com/chingyaoc/fair-mixup | 54 | Fair mixup: Fairness via interpolation | https://scholar.google.com/scholar?cluster=15581530866838341454&hl=en&as_sdt=0,5 | 2 | 2,021 |
Self-supervised Learning from a Multi-view Perspective | 119 | iclr | 8 | 3 | 2023-06-18 09:24:43.459000 | https://github.com/yaohungt/Demystifying_Self_Supervised_Learning | 38 | Self-supervised learning from a multi-view perspective | https://scholar.google.com/scholar?cluster=12546454131517763029&hl=en&as_sdt=0,14 | 6 | 2,021 |
Integrating Categorical Semantics into Unsupervised Domain Translation | 3 | iclr | 0 | 0 | 2023-06-18 09:24:43.662000 | https://github.com/lavoiems/Cats-UDT | 4 | Integrating categorical semantics into unsupervised domain translation | https://scholar.google.com/scholar?cluster=16605089044349710257&hl=en&as_sdt=0,44 | 2 | 2,021 |
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods | 15 | iclr | 3 | 0 | 2023-06-18 09:24:43.865000 | https://github.com/louity/patches | 8 | The unreasonable effectiveness of patches in deep convolutional kernels methods | https://scholar.google.com/scholar?cluster=1695072614668071777&hl=en&as_sdt=0,5 | 4 | 2,021 |
Open Question Answering over Tables and Text | 99 | iclr | 23 | 3 | 2023-06-18 09:24:44.067000 | https://github.com/wenhuchen/OTT-QA | 135 | Open question answering over tables and text | https://scholar.google.com/scholar?cluster=3303883977664528561&hl=en&as_sdt=0,39 | 4 | 2,021 |
Evaluation of Similarity-based Explanations | 31 | iclr | 3 | 2 | 2023-06-18 09:24:44.270000 | https://github.com/k-hanawa/criteria_for_instance_based_explanation | 8 | Evaluation of similarity-based explanations | https://scholar.google.com/scholar?cluster=2157018204021335072&hl=en&as_sdt=0,5 | 3 | 2,021 |
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary | 88 | iclr | 10 | 1 | 2023-06-18 09:24:44.474000 | https://github.com/huanzhang12/ATLA_robust_RL | 42 | Robust reinforcement learning on state observations with learned optimal adversary | https://scholar.google.com/scholar?cluster=16441750250550804230&hl=en&as_sdt=0,14 | 4 | 2,021 |
Hierarchical Autoregressive Modeling for Neural Video Compression | 36 | iclr | 2 | 0 | 2023-06-18 09:24:44.678000 | https://github.com/privateyoung/Youtube-NT | 11 | Hierarchical autoregressive modeling for neural video compression | https://scholar.google.com/scholar?cluster=12525554845016581336&hl=en&as_sdt=0,5 | 2 | 2,021 |
Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits | 38 | iclr | 5 | 1 | 2023-06-18 09:24:44.881000 | https://github.com/jiawangbai/TA-LBF | 16 | Targeted attack against deep neural networks via flipping limited weight bits | https://scholar.google.com/scholar?cluster=14009845567586991922&hl=en&as_sdt=0,5 | 1 | 2,021 |
Generalized Multimodal ELBO | 34 | iclr | 4 | 1 | 2023-06-18 09:24:45.085000 | https://github.com/thomassutter/MoPoE | 17 | Generalized multimodal ELBO | https://scholar.google.com/scholar?cluster=17699698224745360599&hl=en&as_sdt=0,39 | 2 | 2,021 |
Auxiliary Learning by Implicit Differentiation | 28 | iclr | 11 | 0 | 2023-06-18 09:24:45.289000 | https://github.com/AvivNavon/AuxiLearn | 76 | Auxiliary learning by implicit differentiation | https://scholar.google.com/scholar?cluster=5217604319390827754&hl=en&as_sdt=0,5 | 5 | 2,021 |
Adversarially Guided Actor-Critic | 54 | iclr | 8 | 0 | 2023-06-18 09:24:45.491000 | https://github.com/yfletberliac/adversarially-guided-actor-critic | 44 | Adversarially guided actor-critic | https://scholar.google.com/scholar?cluster=15451474207173582523&hl=en&as_sdt=0,44 | 4 | 2,021 |
DARTS-: Robustly Stepping out of Performance Collapse Without Indicators | 103 | iclr | 10 | 4 | 2023-06-18 09:24:45.699000 | https://github.com/Meituan-AutoML/DARTS- | 55 | Darts-: robustly stepping out of performance collapse without indicators | https://scholar.google.com/scholar?cluster=14536849517699271582&hl=en&as_sdt=0,10 | 2 | 2,021 |
Are wider nets better given the same number of parameters? | 37 | iclr | 2 | 0 | 2023-06-18 09:24:45.902000 | https://github.com/google-research/wide-sparse-nets | 18 | Are wider nets better given the same number of parameters? | https://scholar.google.com/scholar?cluster=5708484653398941764&hl=en&as_sdt=0,15 | 5 | 2,021 |
Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks | 101 | iclr | 5 | 0 | 2023-06-18 09:24:46.105000 | https://github.com/Jackn0/snn_optimal_conversion_pipeline | 27 | Optimal conversion of conventional artificial neural networks to spiking neural networks | https://scholar.google.com/scholar?cluster=1643416764815138161&hl=en&as_sdt=0,47 | 1 | 2,021 |
Deep Equals Shallow for ReLU Networks in Kernel Regimes | 51 | iclr | 1 | 0 | 2023-06-18 09:24:46.308000 | https://github.com/albietz/deep_shallow_kernel | 1 | Deep equals shallow for ReLU networks in kernel regimes | https://scholar.google.com/scholar?cluster=9990384037530599388&hl=en&as_sdt=0,5 | 1 | 2,021 |
Early Stopping in Deep Networks: Double Descent and How to Eliminate it | 32 | iclr | 7 | 7 | 2023-06-18 09:24:46.511000 | https://github.com/MLI-lab/early_stopping_double_descent | 12 | Early stopping in deep networks: Double descent and how to eliminate it | https://scholar.google.com/scholar?cluster=7207613062069404274&hl=en&as_sdt=0,5 | 3 | 2,021 |
FairBatch: Batch Selection for Model Fairness | 67 | iclr | 4 | 0 | 2023-06-18 09:24:46.714000 | https://github.com/yuji-roh/fairbatch | 16 | Fairbatch: Batch selection for model fairness | https://scholar.google.com/scholar?cluster=9329551878628232526&hl=en&as_sdt=0,19 | 2 | 2,021 |
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction | 5 | iclr | 3 | 0 | 2023-06-18 09:24:46.917000 | https://github.com/WayneDW/Variance_Reduced_Replica_Exchange_SGMCMC | 8 | Accelerating convergence of replica exchange stochastic gradient MCMC via variance reduction | https://scholar.google.com/scholar?cluster=11364151654891538000&hl=en&as_sdt=0,5 | 3 | 2,021 |
The Importance of Pessimism in Fixed-Dataset Policy Optimization | 111 | iclr | 0 | 1 | 2023-06-18 09:24:47.120000 | https://github.com/jbuckman/tiopifdpo | 6 | The importance of pessimism in fixed-dataset policy optimization | https://scholar.google.com/scholar?cluster=7642597601487950859&hl=en&as_sdt=0,33 | 3 | 2,021 |
Hopfield Networks is All You Need | 242 | iclr | 158 | 8 | 2023-06-18 09:24:47.324000 | https://github.com/ml-jku/hopfield-layers | 1,488 | Hopfield networks is all you need | https://scholar.google.com/scholar?cluster=3659395221954190351&hl=en&as_sdt=0,6 | 42 | 2,021 |
Understanding the failure modes of out-of-distribution generalization | 110 | iclr | 5 | 0 | 2023-06-18 09:24:47.527000 | https://github.com/google-research/OOD-failures | 23 | Understanding the failure modes of out-of-distribution generalization | https://scholar.google.com/scholar?cluster=5584692372209891992&hl=en&as_sdt=0,36 | 5 | 2,021 |
Emergent Road Rules In Multi-Agent Driving Environments | 13 | iclr | 21 | 0 | 2023-06-18 09:24:47.730000 | https://github.com/fidler-lab/social-driving | 130 | Emergent road rules in multi-agent driving environments | https://scholar.google.com/scholar?cluster=11147585939846933269&hl=en&as_sdt=0,33 | 12 | 2,021 |
Wasserstein-2 Generative Networks | 62 | iclr | 4 | 0 | 2023-06-18 09:24:47.934000 | https://github.com/iamalexkorotin/Wasserstein2GenerativeNetworks | 45 | Wasserstein-2 generative networks | https://scholar.google.com/scholar?cluster=5186040077204830092&hl=en&as_sdt=0,23 | 5 | 2,021 |
LEAF: A Learnable Frontend for Audio Classification | 91 | iclr | 50 | 20 | 2023-06-18 09:24:48.138000 | https://github.com/google-research/leaf-audio | 444 | LEAF: A learnable frontend for audio classification | https://scholar.google.com/scholar?cluster=14147422070521797916&hl=en&as_sdt=0,33 | 12 | 2,021 |
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms | 56 | iclr | 12 | 1 | 2023-06-18 09:24:48.342000 | https://github.com/alshedivat/fedpa | 44 | Federated learning via posterior averaging: A new perspective and practical algorithms | https://scholar.google.com/scholar?cluster=2486025806014234529&hl=en&as_sdt=0,32 | 2 | 2,021 |
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval | 593 | iclr | 48 | 13 | 2023-06-18 09:24:48.545000 | https://github.com/microsoft/ANCE | 311 | Approximate nearest neighbor negative contrastive learning for dense text retrieval | https://scholar.google.com/scholar?cluster=8917790448070447494&hl=en&as_sdt=0,33 | 13 | 2,021 |
Auxiliary Task Update Decomposition: the Good, the Bad and the neutral | 11 | iclr | 0 | 0 | 2023-06-18 09:24:48.748000 | https://github.com/ldery/ATTITTUD | 9 | Auxiliary task update decomposition: The good, the bad and the neutral | https://scholar.google.com/scholar?cluster=5872379773640363834&hl=en&as_sdt=0,33 | 2 | 2,021 |
SSD: A Unified Framework for Self-Supervised Outlier Detection | 156 | iclr | 27 | 1 | 2023-06-18 09:24:48.952000 | https://github.com/inspire-group/SSD | 121 | Ssd: A unified framework for self-supervised outlier detection | https://scholar.google.com/scholar?cluster=18087700552913806931&hl=en&as_sdt=0,47 | 4 | 2,021 |
Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning | 24 | iclr | 1 | 3 | 2023-06-18 09:24:49.155000 | https://github.com/valeriechen/ask-your-humans | 9 | Ask your humans: Using human instructions to improve generalization in reinforcement learning | https://scholar.google.com/scholar?cluster=12446456886016968703&hl=en&as_sdt=0,47 | 1 | 2,021 |
Revisiting Few-sample BERT Fine-tuning | 269 | iclr | 14 | 5 | 2023-06-18 09:24:49.360000 | https://github.com/asappresearch/revisit-bert-finetuning | 182 | Revisiting few-sample BERT fine-tuning | https://scholar.google.com/scholar?cluster=4118367966283373449&hl=en&as_sdt=0,29 | 2 | 2,021 |
Tilted Empirical Risk Minimization | 78 | iclr | 9 | 1 | 2023-06-18 09:24:49.563000 | https://github.com/litian96/TERM | 47 | Tilted empirical risk minimization | https://scholar.google.com/scholar?cluster=13273330371410515607&hl=en&as_sdt=0,33 | 3 | 2,021 |
Calibration tests beyond classification | 8 | iclr | 1 | 1 | 2023-06-18 09:24:49.766000 | https://github.com/devmotion/calibration_iclr2021 | 4 | Calibration tests beyond classification | https://scholar.google.com/scholar?cluster=7019919403601581708&hl=en&as_sdt=0,33 | 2 | 2,021 |
You Only Need Adversarial Supervision for Semantic Image Synthesis | 102 | iclr | 55 | 20 | 2023-06-18 09:24:49.970000 | https://github.com/boschresearch/OASIS | 296 | You only need adversarial supervision for semantic image synthesis | https://scholar.google.com/scholar?cluster=11330153460925373123&hl=en&as_sdt=0,32 | 14 | 2,021 |
Learning to Recombine and Resample Data For Compositional Generalization | 61 | iclr | 1 | 0 | 2023-06-18 09:24:50.173000 | https://github.com/ekinakyurek/compgen | 10 | Learning to recombine and resample data for compositional generalization | https://scholar.google.com/scholar?cluster=16034423626440720931&hl=en&as_sdt=0,50 | 2 | 2,021 |
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving | 25 | iclr | 2 | 9 | 2023-06-18 09:24:50.376000 | https://github.com/albertqjiang/INT | 26 | Int: An inequality benchmark for evaluating generalization in theorem proving | https://scholar.google.com/scholar?cluster=2622676809142200746&hl=en&as_sdt=0,33 | 5 | 2,021 |
On the Dynamics of Training Attention Models | 3,925 | iclr | 0 | 0 | 2023-06-18 09:24:50.579000 | https://github.com/haoyelyu/On_the_Dynamics_of_Training_Attention_Models | 1 | Recurrent models of visual attention | https://scholar.google.com/scholar?cluster=4636836599580194602&hl=en&as_sdt=0,22 | 1 | 2,021 |
Contextual Dropout: An Efficient Sample-Dependent Dropout Module | 23 | iclr | 1 | 1 | 2023-06-18 09:24:50.782000 | https://github.com/szhang42/Contextual_dropout_release | 1 | Contextual dropout: An efficient sample-dependent dropout module | https://scholar.google.com/scholar?cluster=17581927588225290546&hl=en&as_sdt=0,44 | 1 | 2,021 |
Mirostat: a Neural Text decoding Algorithm that directly controls perplexity | 23 | iclr | 2 | 1 | 2023-06-18 09:24:50.985000 | https://github.com/basusourya/mirostat | 32 | Mirostat: A neural text decoding algorithm that directly controls perplexity | https://scholar.google.com/scholar?cluster=4013825852088640582&hl=en&as_sdt=0,21 | 2 | 2,021 |
Subsets and Splits