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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 |
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