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Gradient Step Denoiser for convergent Plug-and-Play | 35 | iclr | 4 | 0 | 2023-06-18 09:44:38.996000 | https://github.com/samuro95/gspnp | 14 | Gradient step denoiser for convergent plug-and-play | https://scholar.google.com/scholar?cluster=3194499334349587754&hl=en&as_sdt=0,19 | 4 | 2,022 |
Understanding Dimensional Collapse in Contrastive Self-supervised Learning | 147 | iclr | 6 | 1 | 2023-06-18 09:44:39.205000 | https://github.com/facebookresearch/directclr | 57 | Understanding dimensional collapse in contrastive self-supervised learning | https://scholar.google.com/scholar?cluster=15289790182345311933&hl=en&as_sdt=0,41 | 7 | 2,022 |
RegionViT: Regional-to-Local Attention for Vision Transformers | 95 | iclr | 6 | 2 | 2023-06-18 09:44:39.409000 | https://github.com/IBM/RegionViT | 43 | Regionvit: Regional-to-local attention for vision transformers | https://scholar.google.com/scholar?cluster=17393879915811894634&hl=en&as_sdt=0,5 | 7 | 2,022 |
Quadtree Attention for Vision Transformers | 61 | iclr | 27 | 15 | 2023-06-18 09:44:39.613000 | https://github.com/tangshitao/quadtreeattention | 273 | Quadtree attention for vision transformers | https://scholar.google.com/scholar?cluster=8134043907351506595&hl=en&as_sdt=0,32 | 13 | 2,022 |
What's Wrong with Deep Learning in Tree Search for Combinatorial Optimization | 10 | iclr | 2 | 1 | 2023-06-18 09:44:39.821000 | https://github.com/maxiboether/mis-benchmark-framework | 27 | What's Wrong with Deep Learning in Tree Search for Combinatorial Optimization | https://scholar.google.com/scholar?cluster=18330070821470336106&hl=en&as_sdt=0,32 | 3 | 2,022 |
ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity | 17 | iclr | 4 | 0 | 2023-06-18 09:44:40.034000 | https://github.com/naver/artemis | 36 | Artemis: Attention-based retrieval with text-explicit matching and implicit similarity | https://scholar.google.com/scholar?cluster=15218636624672765176&hl=en&as_sdt=0,21 | 4 | 2,022 |
Fast Differentiable Matrix Square Root | 10 | iclr | 1 | 0 | 2023-06-18 09:44:40.240000 | https://github.com/KingJamesSong/DifferentiableSVD | 47 | Fast differentiable matrix square root | https://scholar.google.com/scholar?cluster=16011321219520846906&hl=en&as_sdt=0,1 | 2 | 2,022 |
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation | 17 | iclr | 7 | 2 | 2023-06-18 09:44:40.444000 | https://github.com/clevercool/SQuant | 146 | SQuant: On-the-fly data-free quantization via diagonal hessian approximation | https://scholar.google.com/scholar?cluster=748228209807839980&hl=en&as_sdt=0,11 | 3 | 2,022 |
Handling Distribution Shifts on Graphs: An Invariance Perspective | 48 | iclr | 6 | 0 | 2023-06-18 09:44:40.650000 | https://github.com/qitianwu/graphood-eerm | 50 | Handling distribution shifts on graphs: An invariance perspective | https://scholar.google.com/scholar?cluster=15550862662340330123&hl=en&as_sdt=0,5 | 3 | 2,022 |
Closed-form Sample Probing for Learning Generative Models in Zero-shot Learning | 3 | iclr | 0 | 0 | 2023-06-18 09:44:40.856000 | https://github.com/cetinsamet/closed-form-sample-probing | 0 | Closed-form sample probing for training generative models in zero-shot learning | https://scholar.google.com/scholar?cluster=11977259761302754277&hl=en&as_sdt=0,5 | 2 | 2,022 |
Steerable Partial Differential Operators for Equivariant Neural Networks | 14 | iclr | 1 | 0 | 2023-06-18 09:44:41.063000 | https://github.com/ejnnr/steerable_pdos | 15 | Steerable partial differential operators for equivariant neural networks | https://scholar.google.com/scholar?cluster=18342593402456805321&hl=en&as_sdt=0,10 | 2 | 2,022 |
Neural Spectral Marked Point Processes | 6 | iclr | 2 | 0 | 2023-06-18 09:44:41.283000 | https://github.com/meowoodie/Neural-Spectral-Marked-Point-Processes | 7 | Neural spectral marked point processes | https://scholar.google.com/scholar?cluster=15895838574872798573&hl=en&as_sdt=0,7 | 2 | 2,022 |
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners | 81 | iclr | 15 | 0 | 2023-06-18 09:44:41.505000 | https://github.com/zjunlp/DART | 108 | Differentiable prompt makes pre-trained language models better few-shot learners | https://scholar.google.com/scholar?cluster=17540526705863454050&hl=en&as_sdt=0,5 | 6 | 2,022 |
OntoProtein: Protein Pretraining With Gene Ontology Embedding | 26 | iclr | 19 | 0 | 2023-06-18 09:44:41.710000 | https://github.com/zjunlp/ontoprotein | 107 | Ontoprotein: Protein pretraining with gene ontology embedding | https://scholar.google.com/scholar?cluster=17820920484975929118&hl=en&as_sdt=0,5 | 7 | 2,022 |
Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection | 12 | iclr | 2 | 0 | 2023-06-18 09:44:41.915000 | https://github.com/jiwei0921/dsu | 12 | Promoting saliency from depth: Deep unsupervised rgb-d saliency detection | https://scholar.google.com/scholar?cluster=15400914580495629111&hl=en&as_sdt=0,5 | 3 | 2,022 |
Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph | 6 | iclr | 2 | 0 | 2023-06-18 09:44:42.119000 | https://github.com/xrenaa/Retriever | 51 | Retriever: Learning content-style representation as a token-level bipartite graph | https://scholar.google.com/scholar?cluster=17348549797304685480&hl=en&as_sdt=0,5 | 18 | 2,022 |
Chemical-Reaction-Aware Molecule Representation Learning | 29 | iclr | 18 | 4 | 2023-06-18 09:44:42.325000 | https://github.com/hwwang55/MolR | 56 | Chemical-reaction-aware molecule representation learning | https://scholar.google.com/scholar?cluster=16867974973581425308&hl=en&as_sdt=0,5 | 1 | 2,022 |
InfinityGAN: Towards Infinite-Pixel Image Synthesis | 27 | iclr | 22 | 11 | 2023-06-18 09:44:42.531000 | https://github.com/hubert0527/infinityGAN | 299 | InfinityGAN: Towards infinite-pixel image synthesis | https://scholar.google.com/scholar?cluster=11409281345563394414&hl=en&as_sdt=0,5 | 32 | 2,022 |
On the Importance of Difficulty Calibration in Membership Inference Attacks | 25 | iclr | 1 | 0 | 2023-06-18 09:44:42.737000 | https://github.com/facebookresearch/calibration_membership | 6 | On the importance of difficulty calibration in membership inference attacks | https://scholar.google.com/scholar?cluster=2933122838404146328&hl=en&as_sdt=0,33 | 5 | 2,022 |
Dual Lottery Ticket Hypothesis | 19 | iclr | 7 | 0 | 2023-06-18 09:44:42.942000 | https://github.com/yueb17/dlth | 25 | Dual lottery ticket hypothesis | https://scholar.google.com/scholar?cluster=3069306637615595875&hl=en&as_sdt=0,33 | 1 | 2,022 |
Neural graphical modelling in continuous-time: consistency guarantees and algorithms | 12 | iclr | 158 | 4 | 2023-06-18 09:44:43.148000 | https://github.com/vanderschaarlab/mlforhealthlabpub | 347 | Neural graphical modelling in continuous-time: consistency guarantees and algorithms | https://scholar.google.com/scholar?cluster=3383946799962251947&hl=en&as_sdt=0,31 | 13 | 2,022 |
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy | 18 | iclr | 94 | 29 | 2023-06-18 09:44:43.356000 | https://github.com/automl/NASLib | 403 | Nas-bench-suite: NAS evaluation is (now) surprisingly easy | https://scholar.google.com/scholar?cluster=4023865038521320162&hl=en&as_sdt=0,5 | 14 | 2,022 |
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation | 85 | iclr | 38 | 11 | 2023-06-18 09:44:43.614000 | https://github.com/cdtrans/cdtrans | 268 | Cdtrans: Cross-domain transformer for unsupervised domain adaptation | https://scholar.google.com/scholar?cluster=9897783945226246229&hl=en&as_sdt=0,21 | 5 | 2,022 |
GradMax: Growing Neural Networks using Gradient Information | 18 | iclr | 5 | 0 | 2023-06-18 09:44:43.819000 | https://github.com/google-research/growneuron | 35 | Gradmax: Growing neural networks using gradient information | https://scholar.google.com/scholar?cluster=11971978084540378903&hl=en&as_sdt=0,34 | 6 | 2,022 |
Random matrices in service of ML footprint: ternary random features with no performance loss | 2 | iclr | 0 | 0 | 2023-06-18 09:44:44.025000 | https://github.com/hafiztiomoko/ternaryrandomfeatures | 0 | Random matrices in service of ML footprint: ternary random features with no performance loss | https://scholar.google.com/scholar?cluster=5706402488540408892&hl=en&as_sdt=0,14 | 1 | 2,022 |
Transformers Can Do Bayesian Inference | 18 | iclr | 11 | 1 | 2023-06-18 09:44:44.230000 | https://github.com/automl/transformerscandobayesianinference | 122 | Transformers can do bayesian inference | https://scholar.google.com/scholar?cluster=1831390603227994904&hl=en&as_sdt=0,5 | 14 | 2,022 |
Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies | 3 | iclr | 2 | 0 | 2023-06-18 09:44:44.436000 | https://github.com/berlinera/dsvae-nes | 1 | Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies | https://scholar.google.com/scholar?cluster=14166545846763510753&hl=en&as_sdt=0,10 | 2 | 2,022 |
Learning Features with Parameter-Free Layers | 3 | iclr | 6 | 0 | 2023-06-18 09:44:44.644000 | https://github.com/naver-ai/pflayer | 83 | Learning Features with Parameter-Free Layers | https://scholar.google.com/scholar?cluster=18180829610140817876&hl=en&as_sdt=0,34 | 7 | 2,022 |
Denoising Likelihood Score Matching for Conditional Score-based Data Generation | 8 | iclr | 1 | 0 | 2023-06-18 09:44:44.848000 | https://github.com/chen-hao-chao/dlsm | 7 | Denoising likelihood score matching for conditional score-based data generation | https://scholar.google.com/scholar?cluster=10811875689229589569&hl=en&as_sdt=0,33 | 3 | 2,022 |
Memory Replay with Data Compression for Continual Learning | 29 | iclr | 0 | 0 | 2023-06-18 09:44:45.055000 | https://github.com/lywang3081/MRDC | 11 | Memory replay with data compression for continual learning | https://scholar.google.com/scholar?cluster=18195224691973743635&hl=en&as_sdt=0,10 | 1 | 2,022 |
RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning | 5 | iclr | 2 | 2 | 2023-06-18 09:44:45.260000 | https://github.com/NVlabs/RelViT | 56 | Relvit: Concept-guided vision transformer for visual relational reasoning | https://scholar.google.com/scholar?cluster=10463631265009137162&hl=en&as_sdt=0,5 | 6 | 2,022 |
ViDT: An Efficient and Effective Fully Transformer-based Object Detector | 46 | iclr | 41 | 16 | 2023-06-18 09:44:45.468000 | https://github.com/naver-ai/vidt | 280 | Vidt: An efficient and effective fully transformer-based object detector | https://scholar.google.com/scholar?cluster=1253153783722573136&hl=en&as_sdt=0,5 | 17 | 2,022 |
BiBERT: Accurate Fully Binarized BERT | 35 | iclr | 4 | 2 | 2023-06-18 09:44:45.673000 | https://github.com/htqin/bibert | 69 | Bibert: Accurate fully binarized bert | https://scholar.google.com/scholar?cluster=5828841794097016283&hl=en&as_sdt=0,23 | 3 | 2,022 |
Representation-Agnostic Shape Fields | 4 | iclr | 1 | 0 | 2023-06-18 09:44:45.878000 | https://github.com/seanywang0408/rasf | 17 | Representation-agnostic shape fields | https://scholar.google.com/scholar?cluster=3096162575637292302&hl=en&as_sdt=0,5 | 2 | 2,022 |
Learning Synthetic Environments and Reward Networks for Reinforcement Learning | 1 | iclr | 3 | 16 | 2023-06-18 09:44:46.083000 | https://github.com/automl/learning_environments | 18 | Learning Synthetic Environments and Reward Networks for Reinforcement Learning | https://scholar.google.com/scholar?cluster=7208756410872374371&hl=en&as_sdt=0,7 | 10 | 2,022 |
Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View | 17 | iclr | 9 | 0 | 2023-06-18 09:44:46.288000 | https://github.com/xrenaa/DisCo | 124 | Learning disentangled representation by exploiting pretrained generative models: A contrastive learning view | https://scholar.google.com/scholar?cluster=5205978200663209990&hl=en&as_sdt=0,1 | 11 | 2,022 |
Towards Building A Group-based Unsupervised Representation Disentanglement Framework | 14 | iclr | 1 | 0 | 2023-06-18 09:44:46.494000 | https://github.com/ThomasMrY/Groupified-VAE | 11 | Towards building a group-based unsupervised representation disentanglement framework | https://scholar.google.com/scholar?cluster=12379032527618028840&hl=en&as_sdt=0,5 | 1 | 2,022 |
Learning Hierarchical Structures with Differentiable Nondeterministic Stacks | 5 | iclr | 0 | 0 | 2023-06-18 09:44:46.698000 | https://github.com/bdusell/nondeterministic-stack-rnn | 14 | Learning hierarchical structures with differentiable nondeterministic stacks | https://scholar.google.com/scholar?cluster=881169137976073427&hl=en&as_sdt=0,39 | 2 | 2,022 |
Sampling with Mirrored Stein Operators | 12 | iclr | 2 | 0 | 2023-06-18 09:44:46.903000 | https://github.com/thjashin/mirror-stein-samplers | 5 | Sampling with mirrored Stein operators | https://scholar.google.com/scholar?cluster=8093287446916276740&hl=en&as_sdt=0,5 | 1 | 2,022 |
RotoGrad: Gradient Homogenization in Multitask Learning | 39 | iclr | 5 | 3 | 2023-06-18 09:44:47.107000 | https://github.com/adrianjav/rotograd | 66 | Rotograd: Gradient homogenization in multitask learning | https://scholar.google.com/scholar?cluster=17548850565658345849&hl=en&as_sdt=0,44 | 3 | 2,022 |
On the Connection between Local Attention and Dynamic Depth-wise Convolution | 44 | iclr | 14 | 4 | 2023-06-18 09:44:47.311000 | https://github.com/atten4vis/demystifylocalvit | 161 | On the connection between local attention and dynamic depth-wise convolution | https://scholar.google.com/scholar?cluster=3348693561656853754&hl=en&as_sdt=0,23 | 4 | 2,022 |
Adversarial Support Alignment | 4 | iclr | 2 | 0 | 2023-06-18 09:44:47.515000 | https://github.com/timgaripov/asa | 19 | Adversarial support alignment | https://scholar.google.com/scholar?cluster=18158530648839344635&hl=en&as_sdt=0,5 | 3 | 2,022 |
Learning meta-features for AutoML | 12 | iclr | 1 | 0 | 2023-06-18 09:44:47.719000 | https://github.com/luxusg1/metabu | 11 | Learning meta-features for automl | https://scholar.google.com/scholar?cluster=9378213080876956800&hl=en&as_sdt=0,5 | 2 | 2,022 |
Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction | 17 | iclr | 20 | 4 | 2023-06-18 09:44:47.922000 | https://github.com/roggirg/AutoBots | 58 | Latent variable sequential set transformers for joint multi-agent motion prediction | https://scholar.google.com/scholar?cluster=1206042525359273292&hl=en&as_sdt=0,33 | 1 | 2,022 |
Deconstructing the Inductive Biases of Hamiltonian Neural Networks | 21 | iclr | 0 | 2 | 2023-06-18 09:44:48.127000 | https://github.com/ngruver/decon-hnn | 10 | Deconstructing the inductive biases of hamiltonian neural networks | https://scholar.google.com/scholar?cluster=301233728507989887&hl=en&as_sdt=0,44 | 4 | 2,022 |
Memorizing Transformers | 63 | iclr | 40 | 9 | 2023-06-18 09:44:48.330000 | https://github.com/lucidrains/memorizing-transformers-pytorch | 538 | Memorizing transformers | https://scholar.google.com/scholar?cluster=12149100013599717090&hl=en&as_sdt=0,47 | 11 | 2,022 |
MT3: Multi-Task Multitrack Music Transcription | 28 | iclr | 149 | 35 | 2023-06-18 09:44:48.535000 | https://github.com/magenta/mt3 | 1,043 | Mt3: Multi-task multitrack music transcription | https://scholar.google.com/scholar?cluster=4757063593798788847&hl=en&as_sdt=0,40 | 28 | 2,022 |
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features | 5 | iclr | 0 | 0 | 2023-06-18 09:44:48.738000 | https://github.com/JiuhaiChen/EBBS | 3 | Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features | https://scholar.google.com/scholar?cluster=12401387776343070829&hl=en&as_sdt=0,5 | 2 | 2,022 |
Geometric and Physical Quantities improve E(3) Equivariant Message Passing | 66 | iclr | 10 | 4 | 2023-06-18 09:44:48.942000 | https://github.com/robdhess/steerable-e3-gnn | 73 | Geometric and physical quantities improve e (3) equivariant message passing | https://scholar.google.com/scholar?cluster=10039670233060190176&hl=en&as_sdt=0,8 | 4 | 2,022 |
Boosting Randomized Smoothing with Variance Reduced Classifiers | 15 | iclr | 0 | 2 | 2023-06-18 09:44:49.147000 | https://github.com/eth-sri/smoothing-ensembles | 8 | Boosting randomized smoothing with variance reduced classifiers | https://scholar.google.com/scholar?cluster=14327532718877741433&hl=en&as_sdt=0,43 | 6 | 2,022 |
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning | 3 | iclr | 0 | 1 | 2023-06-18 09:44:49.350000 | https://github.com/boschresearch/sosp | 1 | Sosp: Efficiently capturing global correlations by second-order structured pruning | https://scholar.google.com/scholar?cluster=7488688580406303390&hl=en&as_sdt=0,14 | 3 | 2,022 |
Relational Multi-Task Learning: Modeling Relations between Data and Tasks | 4 | iclr | 168 | 15 | 2023-06-18 09:44:49.555000 | https://github.com/snap-stanford/graphgym | 1,397 | Relational multi-task learning: Modeling relations between data and tasks | https://scholar.google.com/scholar?cluster=623605199457003104&hl=en&as_sdt=0,44 | 23 | 2,022 |
CoBERL: Contrastive BERT for Reinforcement Learning | 17 | iclr | 613 | 70 | 2023-06-18 09:44:49.758000 | https://github.com/deepmind/dm_control | 3,202 | Coberl: Contrastive bert for reinforcement learning | https://scholar.google.com/scholar?cluster=3823279505832239744&hl=en&as_sdt=0,5 | 127 | 2,022 |
On Bridging Generic and Personalized Federated Learning for Image Classification | 55 | iclr | 0 | 2 | 2023-06-18 09:44:49.962000 | https://github.com/hongyouc/fed-rod | 10 | On bridging generic and personalized federated learning for image classification | https://scholar.google.com/scholar?cluster=3469194395993782827&hl=en&as_sdt=0,5 | 3 | 2,022 |
Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration | 25 | iclr | 5 | 0 | 2023-06-18 09:44:50.165000 | https://github.com/desikrengarajan/logo | 17 | Reinforcement learning with sparse rewards using guidance from offline demonstration | https://scholar.google.com/scholar?cluster=6148886566095169606&hl=en&as_sdt=0,33 | 1 | 2,022 |
Linking Emergent and Natural Languages via Corpus Transfer | 4 | iclr | 3 | 0 | 2023-06-18 09:44:50.370000 | https://github.com/ysymyth/ec-nl | 23 | Linking Emergent and Natural Languages via Corpus Transfer | https://scholar.google.com/scholar?cluster=1456115068072297417&hl=en&as_sdt=0,25 | 3 | 2,022 |
Message Passing Neural PDE Solvers | 79 | iclr | 17 | 0 | 2023-06-18 09:44:50.600000 | https://github.com/brandstetter-johannes/mp-neural-pde-solvers | 59 | Message passing neural PDE solvers | https://scholar.google.com/scholar?cluster=9088135830297201356&hl=en&as_sdt=0,14 | 1 | 2,022 |
Multi-Stage Episodic Control for Strategic Exploration in Text Games | 8 | iclr | 2 | 0 | 2023-06-18 09:44:50.804000 | https://github.com/princeton-nlp/xtx | 13 | Multi-stage episodic control for strategic exploration in text games | https://scholar.google.com/scholar?cluster=10027236272852708486&hl=en&as_sdt=0,5 | 3 | 2,022 |
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning | 25 | iclr | 8 | 0 | 2023-06-18 09:44:51.008000 | https://github.com/adaptive-rl/adarl-code | 20 | Adarl: What, where, and how to adapt in transfer reinforcement learning | https://scholar.google.com/scholar?cluster=6560728254700453684&hl=en&as_sdt=0,22 | 3 | 2,022 |
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking | 64 | iclr | 47 | 12 | 2023-06-18 09:44:51.211000 | https://github.com/octavian-ganea/equidock_public | 182 | Independent se (3)-equivariant models for end-to-end rigid protein docking | https://scholar.google.com/scholar?cluster=4354925472865069663&hl=en&as_sdt=0,31 | 6 | 2,022 |
Towards a Unified View of Parameter-Efficient Transfer Learning | 233 | iclr | 37 | 6 | 2023-06-18 09:44:51.415000 | https://github.com/jxhe/unify-parameter-efficient-tuning | 366 | Towards a unified view of parameter-efficient transfer learning | https://scholar.google.com/scholar?cluster=5204198989920297993&hl=en&as_sdt=0,15 | 7 | 2,022 |
GNN-LM: Language Modeling based on Global Contexts via GNN | 18 | iclr | 5 | 0 | 2023-06-18 09:44:51.619000 | https://github.com/ShannonAI/GNN-LM | 39 | Gnn-lm: Language modeling based on global contexts via gnn | https://scholar.google.com/scholar?cluster=7267447337261309550&hl=en&as_sdt=0,34 | 3 | 2,022 |
Continual Learning with Filter Atom Swapping | 11 | iclr | 3 | 1 | 2023-06-18 09:44:51.823000 | https://github.com/ZichenMiao/CL_Atom_Swapping | 12 | Continual learning with filter atom swapping | https://scholar.google.com/scholar?cluster=12304346311077160974&hl=en&as_sdt=0,33 | 1 | 2,022 |
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning | 27 | iclr | 5 | 1 | 2023-06-18 09:44:52.026000 | https://github.com/zzzace2000/nodegam | 27 | Node-gam: Neural generalized additive model for interpretable deep learning | https://scholar.google.com/scholar?cluster=3759801935043653070&hl=en&as_sdt=0,5 | 3 | 2,022 |
Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100 | 21 | iclr | 6 | 1 | 2023-06-18 09:44:52.229000 | https://github.com/singlasahil14/SOC | 12 | Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100 | https://scholar.google.com/scholar?cluster=11890020481997280688&hl=en&as_sdt=0,40 | 1 | 2,022 |
EntQA: Entity Linking as Question Answering | 23 | iclr | 11 | 3 | 2023-06-18 09:44:52.433000 | https://github.com/wenzhengzhang/entqa | 51 | EntQA: Entity linking as question answering | https://scholar.google.com/scholar?cluster=8005658916202648918&hl=en&as_sdt=0,21 | 2 | 2,022 |
Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems | 25 | iclr | 0 | 0 | 2023-06-18 09:44:52.635000 | https://github.com/lions-epfl/weak-minty-code | 2 | Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems | https://scholar.google.com/scholar?cluster=14926663774187351039&hl=en&as_sdt=0,5 | 3 | 2,022 |
Compositional Attention: Disentangling Search and Retrieval | 11 | iclr | 6 | 1 | 2023-06-18 09:44:52.840000 | https://github.com/sarthmit/compositional-attention | 57 | Compositional attention: Disentangling search and retrieval | https://scholar.google.com/scholar?cluster=1630545213475914915&hl=en&as_sdt=0,10 | 3 | 2,022 |
Contrastive Fine-grained Class Clustering via Generative Adversarial Networks | 8 | iclr | 6 | 1 | 2023-06-18 09:44:53.044000 | https://github.com/naver-ai/c3-gan | 116 | Contrastive fine-grained class clustering via generative adversarial networks | https://scholar.google.com/scholar?cluster=2883627661337586326&hl=en&as_sdt=0,5 | 8 | 2,022 |
Learning Multimodal VAEs through Mutual Supervision | 5 | iclr | 1 | 3 | 2023-06-18 09:44:53.247000 | https://github.com/thwjoy/meme | 5 | Learning multimodal VAEs through mutual supervision | https://scholar.google.com/scholar?cluster=8935371068156409953&hl=en&as_sdt=0,5 | 3 | 2,022 |
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation | 10 | iclr | 0 | 2 | 2023-06-18 09:44:53.451000 | https://github.com/deepmind/constrained_optidice | 7 | Coptidice: Offline constrained reinforcement learning via stationary distribution correction estimation | https://scholar.google.com/scholar?cluster=10582988785015243548&hl=en&as_sdt=0,10 | 5 | 2,022 |
ViTGAN: Training GANs with Vision Transformers | 100 | iclr | 6 | 7 | 2023-06-18 09:44:53.659000 | https://github.com/mlpc-ucsd/ViTGAN | 32 | Vitgan: Training gans with vision transformers | https://scholar.google.com/scholar?cluster=11425422721644021530&hl=en&as_sdt=0,44 | 3 | 2,022 |
TRGP: Trust Region Gradient Projection for Continual Learning | 20 | iclr | 2 | 1 | 2023-06-18 09:44:53.863000 | https://github.com/LYang-666/TRGP | 11 | Trgp: Trust region gradient projection for continual learning | https://scholar.google.com/scholar?cluster=6594331056177251128&hl=en&as_sdt=0,39 | 2 | 2,022 |
Learning Long-Term Reward Redistribution via Randomized Return Decomposition | 8 | iclr | 0 | 0 | 2023-06-18 09:44:54.068000 | https://github.com/stilwell-git/randomized-return-decomposition | 12 | Learning long-term reward redistribution via randomized return decomposition | https://scholar.google.com/scholar?cluster=12389513535108604835&hl=en&as_sdt=0,47 | 1 | 2,022 |
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series | 20 | iclr | 17 | 4 | 2023-06-18 09:44:54.274000 | https://github.com/enyandai/ganf | 93 | Graph-augmented normalizing flows for anomaly detection of multiple time series | https://scholar.google.com/scholar?cluster=14229520023541942069&hl=en&as_sdt=0,5 | 3 | 2,022 |
On the Importance of Firth Bias Reduction in Few-Shot Classification | 9 | iclr | 0 | 0 | 2023-06-18 09:44:54.478000 | https://github.com/ehsansaleh/firth_bias_reduction | 8 | On the importance of firth bias reduction in few-shot classification | https://scholar.google.com/scholar?cluster=9186667972571213142&hl=en&as_sdt=0,47 | 3 | 2,022 |
Towards Understanding the Data Dependency of Mixup-style Training | 8 | iclr | 1 | 0 | 2023-06-18 09:44:54.683000 | https://github.com/2014mchidamb/mixup-data-dependency | 0 | Towards understanding the data dependency of mixup-style training | https://scholar.google.com/scholar?cluster=13244705498491864959&hl=en&as_sdt=0,44 | 1 | 2,022 |
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion | 88 | iclr | 11 | 7 | 2023-06-18 09:44:54.886000 | https://github.com/nv-tlabs/CLD-SGM | 168 | Score-based generative modeling with critically-damped langevin diffusion | https://scholar.google.com/scholar?cluster=1032753694243444141&hl=en&as_sdt=0,33 | 27 | 2,022 |
Controlling Directions Orthogonal to a Classifier | 10 | iclr | 5 | 0 | 2023-06-18 09:44:55.090000 | https://github.com/newbeeer/orthogonal_classifier | 34 | Controlling directions orthogonal to a classifier | https://scholar.google.com/scholar?cluster=14918052753119850605&hl=en&as_sdt=0,5 | 3 | 2,022 |
R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning | 3 | iclr | 2 | 0 | 2023-06-18 09:44:55.294000 | https://github.com/sluxsr/r5_graph_reasoning | 2 | R5: Rule discovery with reinforced and recurrent relational reasoning | https://scholar.google.com/scholar?cluster=13510369297360682676&hl=en&as_sdt=0,32 | 1 | 2,022 |
Lossless Compression with Probabilistic Circuits | 11 | iclr | 0 | 0 | 2023-06-18 09:44:55.498000 | https://github.com/juice-jl/pressedjuice.jl | 11 | Lossless compression with probabilistic circuits | https://scholar.google.com/scholar?cluster=13531638226043466967&hl=en&as_sdt=0,48 | 4 | 2,022 |
$\mathrm{SO}(2)$-Equivariant Reinforcement Learning | 1 | iclr | 2 | 0 | 2023-06-18 09:44:55.703000 | https://github.com/pointW/equi_rl | 23 | -Equivariant Reinforcement Learning | https://scholar.google.com/scholar?cluster=4984868879021481594&hl=en&as_sdt=0,5 | 2 | 2,022 |
Responsible Disclosure of Generative Models Using Scalable Fingerprinting | 24 | iclr | 3 | 0 | 2023-06-18 09:44:55.906000 | https://github.com/ningyu1991/ScalableGANFingerprints | 23 | Responsible disclosure of generative models using scalable fingerprinting | https://scholar.google.com/scholar?cluster=5724642916059035277&hl=en&as_sdt=0,44 | 4 | 2,022 |
Possibility Before Utility: Learning And Using Hierarchical Affordances | 1 | iclr | 1 | 0 | 2023-06-18 09:44:56.109000 | https://github.com/robbycostales/hal | 13 | Possibility Before Utility: Learning And Using Hierarchical Affordances | https://scholar.google.com/scholar?cluster=1637590798034368393&hl=en&as_sdt=0,5 | 2 | 2,022 |
Half-Inverse Gradients for Physical Deep Learning | 5 | iclr | 0 | 0 | 2023-06-18 09:44:56.312000 | https://github.com/tum-pbs/half-inverse-gradients | 14 | Half-inverse gradients for physical deep learning | https://scholar.google.com/scholar?cluster=1729142096110683757&hl=en&as_sdt=0,44 | 2 | 2,022 |
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits | 10 | iclr | 2 | 0 | 2023-06-18 09:44:56.515000 | https://github.com/banyikun/ee-net-iclr-2022 | 11 | EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits | https://scholar.google.com/scholar?cluster=959574665974730167&hl=en&as_sdt=0,5 | 1 | 2,022 |
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective | 8 | iclr | 3 | 0 | 2023-06-18 09:44:56.719000 | https://github.com/damon-demon/black-box-defense | 18 | How to robustify black-box ml models? a zeroth-order optimization perspective | https://scholar.google.com/scholar?cluster=8309073291494301716&hl=en&as_sdt=0,5 | 2 | 2,022 |
RelaxLoss: Defending Membership Inference Attacks without Losing Utility | 9 | iclr | 6 | 0 | 2023-06-18 09:44:56.922000 | https://github.com/DingfanChen/RelaxLoss | 36 | RelaxLoss: defending membership inference attacks without losing utility | https://scholar.google.com/scholar?cluster=6734125501477574187&hl=en&as_sdt=0,5 | 1 | 2,022 |
Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design | 22 | iclr | 16 | 1 | 2023-06-18 09:44:57.125000 | https://github.com/wenhao-gao/SynNet | 67 | Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design | https://scholar.google.com/scholar?cluster=15831555537133301162&hl=en&as_sdt=0,11 | 4 | 2,022 |
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation | 4 | iclr | 2 | 0 | 2023-06-18 09:44:57.329000 | https://github.com/rmclarke/optimisingweightupdatehyperparameters | 9 | Scalable one-pass optimisation of high-dimensional weight-update hyperparameters by implicit differentiation | https://scholar.google.com/scholar?cluster=13151691768844954794&hl=en&as_sdt=0,7 | 1 | 2,022 |
Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation | 13 | iclr | 3 | 1 | 2023-06-18 09:44:57.532000 | https://github.com/montrealrobotics/iv_rl | 29 | Sample efficient deep reinforcement learning via uncertainty estimation | https://scholar.google.com/scholar?cluster=8416439116779187759&hl=en&as_sdt=0,5 | 1 | 2,022 |
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions | 14 | iclr | 3 | 0 | 2023-06-18 09:44:57.736000 | https://github.com/n-gao/pesnet | 20 | Ab-initio potential energy surfaces by pairing GNNs with neural wave functions | https://scholar.google.com/scholar?cluster=16901851478491451308&hl=en&as_sdt=0,5 | 2 | 2,022 |
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data | 8 | iclr | 1 | 1 | 2023-06-18 09:44:57.939000 | https://github.com/haoang97/medi | 15 | Meta discovery: Learning to discover novel classes given very limited data | https://scholar.google.com/scholar?cluster=11348139324456569930&hl=en&as_sdt=0,33 | 1 | 2,022 |
Constrained Policy Optimization via Bayesian World Models | 15 | iclr | 11 | 0 | 2023-06-18 09:44:58.142000 | https://github.com/yardenas/la-mbda | 25 | Constrained policy optimization via bayesian world models | https://scholar.google.com/scholar?cluster=15728158487087331451&hl=en&as_sdt=0,5 | 2 | 2,022 |
Generalized Decision Transformer for Offline Hindsight Information Matching | 44 | iclr | 3 | 4 | 2023-06-18 09:44:58.345000 | https://github.com/frt03/generalized_dt | 54 | Generalized decision transformer for offline hindsight information matching | https://scholar.google.com/scholar?cluster=4011968196773384178&hl=en&as_sdt=0,5 | 0 | 2,022 |
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting | 10 | iclr | 7 | 1 | 2023-06-18 09:44:58.549000 | https://github.com/weifantt/depts | 34 | DEPTS: deep expansion learning for periodic time series forecasting | https://scholar.google.com/scholar?cluster=17674123888632220585&hl=en&as_sdt=0,5 | 2 | 2,022 |
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions | 20 | iclr | 0 | 0 | 2023-06-18 09:44:58.755000 | https://github.com/borgwardtlab/ggme | 12 | Evaluation metrics for graph generative models: Problems, pitfalls, and practical solutions | https://scholar.google.com/scholar?cluster=2895320049488779805&hl=en&as_sdt=0,23 | 5 | 2,022 |
Context-Aware Sparse Deep Coordination Graphs | 14 | iclr | 3 | 1 | 2023-06-18 09:44:58.959000 | https://github.com/tonghanwang/casec-maco-benchmark | 11 | Context-aware sparse deep coordination graphs | https://scholar.google.com/scholar?cluster=17498858288824989874&hl=en&as_sdt=0,33 | 1 | 2,022 |
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models | 27 | iclr | 17 | 11 | 2023-06-18 09:44:59.163000 | https://github.com/HazyResearch/pixelfly | 127 | Pixelated butterfly: Simple and efficient sparse training for neural network models | https://scholar.google.com/scholar?cluster=1108492014641938411&hl=en&as_sdt=0,5 | 22 | 2,022 |
8-bit Optimizers via Block-wise Quantization | 30 | iclr | 38 | 11 | 2023-06-18 09:44:59.368000 | https://github.com/facebookresearch/bitsandbytes | 712 | 8-bit optimizers via block-wise quantization | https://scholar.google.com/scholar?cluster=5491820601242999587&hl=en&as_sdt=0,44 | 14 | 2,022 |