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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
|
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