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Hyper-SAGNN: a self-attention based graph neural network for hypergraphs | 128 | iclr | 19 | 0 | 2023-06-18 09:10:26.446000 | https://github.com/ma-compbio/Hyper-SAGNN | 68 | Hyper-SAGNN: a self-attention based graph neural network for hypergraphs | https://scholar.google.com/scholar?cluster=10735269367403451355&hl=en&as_sdt=0,36 | 4 | 2,020 |
Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks | 28 | iclr | 0 | 1 | 2023-06-18 09:10:26.655000 | https://github.com/JoonyoungYi/sparsity-normalization | 6 | Why not to use zero imputation? correcting sparsity bias in training neural networks | https://scholar.google.com/scholar?cluster=363482687084089467&hl=en&as_sdt=0,47 | 4 | 2,020 |
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification | 916 | iclr | 73 | 13 | 2023-06-18 09:10:26.862000 | https://github.com/DropEdge/DropEdge | 434 | Dropedge: Towards deep graph convolutional networks on node classification | https://scholar.google.com/scholar?cluster=16127626475319244243&hl=en&as_sdt=0,36 | 11 | 2,020 |
Masked Based Unsupervised Content Transfer | 52 | iclr | 9 | 0 | 2023-06-18 09:10:27.066000 | https://github.com/rmokady/mbu-content-tansfer | 42 | A hierarchical reinforced sequence operation method for unsupervised text style transfer | https://scholar.google.com/scholar?cluster=10160450979699237379&hl=en&as_sdt=0,33 | 6 | 2,020 |
Learning Robust Representations via Multi-View Information Bottleneck | 144 | iclr | 16 | 1 | 2023-06-18 09:10:27.332000 | https://github.com/mfederici/Multi-View-Information-Bottleneck | 99 | Learning robust representations via multi-view information bottleneck | https://scholar.google.com/scholar?cluster=11405202326075018962&hl=en&as_sdt=0,33 | 2 | 2,020 |
Deep probabilistic subsampling for task-adaptive compressed sensing | 29 | iclr | 3 | 0 | 2023-06-18 09:10:27.536000 | https://github.com/IamHuijben/Deep-Probabilistic-Subsampling | 18 | Deep probabilistic subsampling for task-adaptive compressed sensing | https://scholar.google.com/scholar?cluster=10812881230787929312&hl=en&as_sdt=0,5 | 2 | 2,020 |
Learning to Guide Random Search | 15 | iclr | 6 | 1 | 2023-06-18 09:10:27.773000 | https://github.com/intel-isl/LMRS | 40 | Learning to guide random search | https://scholar.google.com/scholar?cluster=10046802470639742746&hl=en&as_sdt=0,5 | 11 | 2,020 |
Lagrangian Fluid Simulation with Continuous Convolutions | 124 | iclr | 251 | 9 | 2023-06-18 09:10:27.976000 | https://github.com/InteractiveComputerGraphics/SPlisHSPlasH | 1,287 | Lagrangian fluid simulation with continuous convolutions | https://scholar.google.com/scholar?cluster=1663443529429747125&hl=en&as_sdt=0,33 | 68 | 2,020 |
Learning To Explore Using Active Neural SLAM | 349 | iclr | 130 | 5 | 2023-06-18 09:10:28.179000 | https://github.com/devendrachaplot/Neural-SLAM | 633 | Learning to explore using active neural slam | https://scholar.google.com/scholar?cluster=11696547235753024845&hl=en&as_sdt=0,10 | 23 | 2,020 |
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness Against Adversarial Attacks | 55 | iclr | 6 | 0 | 2023-06-18 09:10:28.383000 | https://github.com/sancharisen/EMPIR | 3 | Empir: Ensembles of mixed precision deep networks for increased robustness against adversarial attacks | https://scholar.google.com/scholar?cluster=16573248157245653901&hl=en&as_sdt=0,5 | 4 | 2,020 |
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel | 45 | iclr | 2 | 0 | 2023-06-18 09:10:28.585000 | https://github.com/leaf-ai/rio-paper | 6 | Quantifying point-prediction uncertainty in neural networks via residual estimation with an i/o kernel | https://scholar.google.com/scholar?cluster=10327919157136760182&hl=en&as_sdt=0,33 | 11 | 2,020 |
B-Spline CNNs on Lie groups | 104 | iclr | 3 | 0 | 2023-06-18 09:10:28.788000 | https://github.com/ebekkers/gsplinets | 46 | B-spline cnns on lie groups | https://scholar.google.com/scholar?cluster=14711713420421113660&hl=en&as_sdt=0,5 | 6 | 2,020 |
Neural Outlier Rejection for Self-Supervised Keypoint Learning | 21 | iclr | 34 | 10 | 2023-06-18 09:10:28.992000 | https://github.com/TRI-ML/KP2D | 164 | Neural outlier rejection for self-supervised keypoint learning | https://scholar.google.com/scholar?cluster=823859441730123149&hl=en&as_sdt=0,5 | 17 | 2,020 |
RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments | 132 | iclr | 22 | 6 | 2023-06-18 09:10:29.195000 | https://github.com/facebookresearch/impact-driven-exploration | 119 | Ride: Rewarding impact-driven exploration for procedurally-generated environments | https://scholar.google.com/scholar?cluster=220681399532996329&hl=en&as_sdt=0,34 | 9 | 2,020 |
Low-dimensional statistical manifold embedding of directed graphs | 5 | iclr | 0 | 0 | 2023-06-18 09:10:29.399000 | https://github.com/funket/dinet_public | 2 | Low-dimensional statistical manifold embedding of directed graphs | https://scholar.google.com/scholar?cluster=9660939784062408067&hl=en&as_sdt=0,14 | 2 | 2,020 |
Efficient Probabilistic Logic Reasoning with Graph Neural Networks | 100 | iclr | 23 | 5 | 2023-06-18 09:10:29.601000 | https://github.com/expressGNN/ExpressGNN | 91 | Efficient probabilistic logic reasoning with graph neural networks | https://scholar.google.com/scholar?cluster=12549090467067040217&hl=en&as_sdt=0,33 | 2 | 2,020 |
GraphSAINT: Graph Sampling Based Inductive Learning Method | 666 | iclr | 79 | 4 | 2023-06-18 09:10:29.805000 | https://github.com/GraphSAINT/GraphSAINT | 407 | Graphsaint: Graph sampling based inductive learning method | https://scholar.google.com/scholar?cluster=4707766140408831355&hl=en&as_sdt=0,26 | 8 | 2,020 |
Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators | 2 | iclr | 3 | 5 | 2023-06-18 09:10:30.007000 | https://github.com/f90/FactorGAN | 32 | Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators | https://scholar.google.com/scholar?cluster=8246776617616848513&hl=en&as_sdt=0,10 | 4 | 2,020 |
Decentralized Deep Learning with Arbitrary Communication Compression | 175 | iclr | 17 | 0 | 2023-06-18 09:10:30.211000 | https://github.com/epfml/ChocoSGD | 49 | Decentralized deep learning with arbitrary communication compression | https://scholar.google.com/scholar?cluster=11705017815367904988&hl=en&as_sdt=0,48 | 7 | 2,020 |
On the Relationship between Self-Attention and Convolutional Layers | 438 | iclr | 130 | 6 | 2023-06-18 09:10:30.415000 | https://github.com/epfml/attention-cnn | 1,013 | On the relationship between self-attention and convolutional layers | https://scholar.google.com/scholar?cluster=11977726124453844540&hl=en&as_sdt=0,33 | 27 | 2,020 |
Structured Object-Aware Physics Prediction for Video Modeling and Planning | 50 | iclr | 8 | 2 | 2023-06-18 09:10:30.618000 | https://github.com/jlko/STOVE | 31 | Structured object-aware physics prediction for video modeling and planning | https://scholar.google.com/scholar?cluster=9673300822333166750&hl=en&as_sdt=0,41 | 5 | 2,020 |
Incorporating BERT into Neural Machine Translation | 354 | iclr | 99 | 24 | 2023-06-18 09:10:30.821000 | https://github.com/bert-nmt/bert-nmt | 336 | Incorporating bert into neural machine translation | https://scholar.google.com/scholar?cluster=2826043205996388394&hl=en&as_sdt=0,6 | 10 | 2,020 |
MMA Training: Direct Input Space Margin Maximization through Adversarial Training | 224 | iclr | 10 | 0 | 2023-06-18 09:10:31.025000 | https://github.com/BorealisAI/mma_training | 33 | Mma training: Direct input space margin maximization through adversarial training | https://scholar.google.com/scholar?cluster=2454066962339603131&hl=en&as_sdt=0,19 | 9 | 2,020 |
Meta-learning curiosity algorithms | 47 | iclr | 18 | 2 | 2023-06-18 09:10:31.228000 | https://github.com/mfranzs/meta-learning-curiosity-algorithms | 78 | Meta-learning curiosity algorithms | https://scholar.google.com/scholar?cluster=957030808144457280&hl=en&as_sdt=0,5 | 5 | 2,020 |
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning | 168 | iclr | 26 | 1 | 2023-06-18 09:10:31.443000 | https://github.com/lmzintgraf/varibad | 146 | Varibad: A very good method for bayes-adaptive deep rl via meta-learning | https://scholar.google.com/scholar?cluster=4911534686383009186&hl=en&as_sdt=0,33 | 7 | 2,020 |
Lookahead: A Far-sighted Alternative of Magnitude-based Pruning | 69 | iclr | 7 | 3 | 2023-06-18 09:10:31.647000 | https://github.com/alinlab/lookahead_pruning | 32 | Lookahead: A far-sighted alternative of magnitude-based pruning | https://scholar.google.com/scholar?cluster=2120869474011210882&hl=en&as_sdt=0,5 | 4 | 2,020 |
Demystifying Inter-Class Disentanglement | 45 | iclr | 1 | 1 | 2023-06-18 09:10:31.850000 | https://github.com/avivga/lord | 6 | Demystifying inter-class disentanglement | https://scholar.google.com/scholar?cluster=4997623727964047990&hl=en&as_sdt=0,33 | 2 | 2,020 |
Mixed-curvature Variational Autoencoders | 24 | iclr | 13 | 2 | 2023-06-18 09:10:32.053000 | https://github.com/oskopek/mvae | 57 | Mixed-curvature variational autoencoders | https://scholar.google.com/scholar?cluster=4577288345206475501&hl=en&as_sdt=0,33 | 5 | 2,020 |
BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations | 41 | iclr | 1 | 1 | 2023-06-18 09:10:32.257000 | https://github.com/Hyungjun-K1m/BinaryDuo | 8 | Binaryduo: Reducing gradient mismatch in binary activation network by coupling binary activations | https://scholar.google.com/scholar?cluster=14477900274189098502&hl=en&as_sdt=0,39 | 2 | 2,020 |
BayesOpt Adversarial Attack | 67 | iclr | 3 | 2 | 2023-06-18 09:10:32.462000 | https://github.com/rubinxin/BayesOpt_Attack | 32 | Bayesopt adversarial attack | https://scholar.google.com/scholar?cluster=45917786652802815&hl=en&as_sdt=0,33 | 2 | 2,020 |
Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies | 35 | iclr | 2 | 0 | 2023-06-18 09:10:32.665000 | https://github.com/srsohn/msgi | 16 | Meta reinforcement learning with autonomous inference of subtask dependencies | https://scholar.google.com/scholar?cluster=15507319353031290390&hl=en&as_sdt=0,33 | 5 | 2,020 |
Dynamics-Aware Embeddings | 42 | iclr | 4 | 0 | 2023-06-18 09:10:32.869000 | https://github.com/dyne-submission/dynamics-aware-embeddings | 14 | Dynamics-aware embeddings | https://scholar.google.com/scholar?cluster=8354834388426273229&hl=en&as_sdt=0,33 | 3 | 2,020 |
AdvectiveNet: An Eulerian-Lagrangian Fluidic Reservoir for Point Cloud Processing | 11 | iclr | 2 | 0 | 2023-06-18 09:10:33.077000 | https://github.com/DIUDIUDIUDIUDIU/AdvectiveNet-An-Eulerian-Lagrangian-Fluidic-Reservoir-for-Point-Cloud-Processing | 7 | Advectivenet: An eulerian-lagrangian fluidic reservoir for point cloud processing | https://scholar.google.com/scholar?cluster=16984583145926125597&hl=en&as_sdt=0,5 | 2 | 2,020 |
Fair Resource Allocation in Federated Learning | 542 | iclr | 56 | 2 | 2023-06-18 09:10:33.291000 | https://github.com/litian96/fair_flearn | 209 | Fair resource allocation in federated learning | https://scholar.google.com/scholar?cluster=15902848371437893934&hl=en&as_sdt=0,43 | 6 | 2,020 |
Training binary neural networks with real-to-binary convolutions | 168 | iclr | 2 | 3 | 2023-06-18 09:10:33.494000 | https://github.com/brais-martinez/real2binary | 35 | Training binary neural networks with real-to-binary convolutions | https://scholar.google.com/scholar?cluster=6977393399937358089&hl=en&as_sdt=0,11 | 8 | 2,020 |
Permutation Equivariant Models for Compositional Generalization in Language | 75 | iclr | 8 | 0 | 2023-06-18 09:10:33.698000 | https://github.com/facebookresearch/Permutation-Equivariant-Seq2Seq | 26 | Permutation equivariant models for compositional generalization in language | https://scholar.google.com/scholar?cluster=5726550999314038954&hl=en&as_sdt=0,7 | 8 | 2,020 |
Continual learning with hypernetworks | 247 | iclr | 15 | 0 | 2023-06-18 09:10:33.901000 | https://github.com/chrhenning/hypercl | 140 | Continual learning with hypernetworks | https://scholar.google.com/scholar?cluster=12864438704892139972&hl=en&as_sdt=0,33 | 6 | 2,020 |
Variational Template Machine for Data-to-Text Generation | 41 | iclr | 8 | 1 | 2023-06-18 09:10:34.104000 | https://github.com/ReneeYe/VariationalTemplateMachine | 29 | Variational template machine for data-to-text generation | https://scholar.google.com/scholar?cluster=7425104340562846421&hl=en&as_sdt=0,33 | 3 | 2,020 |
Memory-Based Graph Networks | 63 | iclr | 21 | 5 | 2023-06-18 09:10:34.307000 | https://github.com/amirkhas/GraphMemoryNet | 100 | Memory-based graph networks | https://scholar.google.com/scholar?cluster=8513021522669466053&hl=en&as_sdt=0,33 | 5 | 2,020 |
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty | 828 | iclr | 159 | 3 | 2023-06-18 09:10:34.510000 | https://github.com/google-research/augmix | 915 | Augmix: A simple data processing method to improve robustness and uncertainty | https://scholar.google.com/scholar?cluster=10820297852320096780&hl=en&as_sdt=0,33 | 30 | 2,020 |
AtomNAS: Fine-Grained End-to-End Neural Architecture Search | 102 | iclr | 21 | 3 | 2023-06-18 09:10:34.714000 | https://github.com/meijieru/AtomNAS | 224 | Atomnas: Fine-grained end-to-end neural architecture search | https://scholar.google.com/scholar?cluster=16282779625023333674&hl=en&as_sdt=0,33 | 7 | 2,020 |
Expected Information Maximization: Using the I-Projection for Mixture Density Estimation | 10 | iclr | 2 | 0 | 2023-06-18 09:10:34.917000 | https://github.com/pbecker93/ExpectedInformationMaximization | 6 | Expected information maximization: Using the i-projection for mixture density estimation | https://scholar.google.com/scholar?cluster=10322383053162964662&hl=en&as_sdt=0,34 | 3 | 2,020 |
On the interaction between supervision and self-play in emergent communication | 48 | iclr | 2 | 1 | 2023-06-18 09:10:35.119000 | https://github.com/backpropper/s2p | 15 | On the interaction between supervision and self-play in emergent communication | https://scholar.google.com/scholar?cluster=3074457436364179887&hl=en&as_sdt=0,47 | 3 | 2,020 |
Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings | 28 | iclr | 12 | 2 | 2023-06-18 09:10:35.323000 | https://github.com/visinf/lnfmm | 32 | Latent normalizing flows for many-to-many cross-domain mappings | https://scholar.google.com/scholar?cluster=3579800435067088843&hl=en&as_sdt=0,44 | 3 | 2,020 |
Lite Transformer with Long-Short Range Attention | 213 | iclr | 77 | 9 | 2023-06-18 09:10:35.526000 | https://github.com/mit-han-lab/lite-transformer | 574 | Lite transformer with long-short range attention | https://scholar.google.com/scholar?cluster=417738905489358302&hl=en&as_sdt=0,33 | 22 | 2,020 |
Compositional Language Continual Learning | 24 | iclr | 5 | 0 | 2023-06-18 09:10:35.728000 | https://github.com/yli1/CLCL | 17 | Compositional language continual learning | https://scholar.google.com/scholar?cluster=7117391709673102792&hl=en&as_sdt=0,5 | 1 | 2,020 |
End to End Trainable Active Contours via Differentiable Rendering | 28 | iclr | 10 | 4 | 2023-06-18 09:10:35.932000 | https://github.com/shirgur/ACDRNet | 81 | End to end trainable active contours via differentiable rendering | https://scholar.google.com/scholar?cluster=4625537332937451422&hl=en&as_sdt=0,43 | 6 | 2,020 |
Provable Filter Pruning for Efficient Neural Networks | 127 | iclr | 22 | 9 | 2023-06-18 09:10:36.135000 | https://github.com/lucaslie/provable_pruning | 146 | Provable filter pruning for efficient neural networks | https://scholar.google.com/scholar?cluster=9217069157983955160&hl=en&as_sdt=0,5 | 5 | 2,020 |
Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories | 34 | iclr | 4 | 0 | 2023-06-18 09:10:36.338000 | https://github.com/tiangeluo/Learning-to-Group | 35 | Learning to group: A bottom-up framework for 3d part discovery in unseen categories | https://scholar.google.com/scholar?cluster=11555751649018705803&hl=en&as_sdt=0,11 | 6 | 2,020 |
Discriminative Particle Filter Reinforcement Learning for Complex Partial observations | 30 | iclr | 1 | 2 | 2023-06-18 09:10:36.541000 | https://github.com/Yusufma03/DPFRL | 24 | Discriminative particle filter reinforcement learning for complex partial observations | https://scholar.google.com/scholar?cluster=1615417312084406584&hl=en&as_sdt=0,33 | 5 | 2,020 |
Learning to Move with Affordance Maps | 21 | iclr | 2 | 0 | 2023-06-18 09:10:36.745000 | https://github.com/wqi/A2L | 32 | Learning to move with affordance maps | https://scholar.google.com/scholar?cluster=10625760242588523450&hl=en&as_sdt=0,11 | 3 | 2,020 |
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning | 240 | iclr | 22 | 3 | 2023-06-18 09:10:36.948000 | https://github.com/bayesgroup/pytorch-ensembles | 219 | Pitfalls of in-domain uncertainty estimation and ensembling in deep learning | https://scholar.google.com/scholar?cluster=6945290947528515507&hl=en&as_sdt=0,33 | 15 | 2,020 |
Deep Orientation Uncertainty Learning based on a Bingham Loss | 50 | iclr | 8 | 0 | 2023-06-18 09:10:37.151000 | https://github.com/igilitschenski/deep_bingham | 28 | Deep orientation uncertainty learning based on a bingham loss | https://scholar.google.com/scholar?cluster=2663295630618004041&hl=en&as_sdt=0,31 | 3 | 2,020 |
Mixed Precision DNNs: All you need is a good parametrization | 124 | iclr | 57 | 11 | 2023-06-18 09:10:37.355000 | https://github.com/sony/ai-research-code | 315 | Mixed precision dnns: All you need is a good parametrization | https://scholar.google.com/scholar?cluster=4816865987143977033&hl=en&as_sdt=0,41 | 32 | 2,020 |
Extreme Classification via Adversarial Softmax Approximation | 22 | iclr | 4 | 1 | 2023-06-18 09:10:37.558000 | https://github.com/mandt-lab/adversarial-negative-sampling | 14 | Extreme classification via adversarial softmax approximation | https://scholar.google.com/scholar?cluster=14613263140871789751&hl=en&as_sdt=0,33 | 4 | 2,020 |
Learning Nearly Decomposable Value Functions Via Communication Minimization | 81 | iclr | 14 | 8 | 2023-06-18 09:10:37.761000 | https://github.com/TonghanWang/NDQ | 73 | Learning nearly decomposable value functions via communication minimization | https://scholar.google.com/scholar?cluster=9765925761850787056&hl=en&as_sdt=0,43 | 5 | 2,020 |
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection | 53 | iclr | 5 | 1 | 2023-06-18 09:10:37.965000 | https://github.com/dmzou/RSRAE | 36 | Robust subspace recovery layer for unsupervised anomaly detection | https://scholar.google.com/scholar?cluster=11513209509503726282&hl=en&as_sdt=0,39 | 1 | 2,020 |
Learning to Coordinate Manipulation Skills via Skill Behavior Diversification | 46 | iclr | 10 | 2 | 2023-06-18 09:10:38.169000 | https://github.com/clvrai/coordination | 39 | Learning to coordinate manipulation skills via skill behavior diversification | https://scholar.google.com/scholar?cluster=5168095143260669466&hl=en&as_sdt=0,11 | 9 | 2,020 |
NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search | 147 | iclr | 14 | 3 | 2023-06-18 09:10:38.374000 | https://github.com/automl/nasbench-1shot1 | 66 | Nas-bench-1shot1: Benchmarking and dissecting one-shot neural architecture search | https://scholar.google.com/scholar?cluster=14286994733629357547&hl=en&as_sdt=0,19 | 9 | 2,020 |
How to 0wn the NAS in Your Spare Time | 29 | iclr | 0 | 0 | 2023-06-18 09:10:38.578000 | https://github.com/Sanghyun-Hong/How-to-0wn-NAS-in-Your-Spare-Time | 1 | How to 0wn nas in your spare time | https://scholar.google.com/scholar?cluster=6624307467439583182&hl=en&as_sdt=0,5 | 3 | 2,020 |
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation | 194 | iclr | 23 | 13 | 2023-06-18 09:10:38.783000 | https://github.com/nitin-rathi/hybrid-snn-conversion | 78 | Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation | https://scholar.google.com/scholar?cluster=2336999671459388564&hl=en&as_sdt=0,33 | 7 | 2,020 |
Breaking Certified Defenses: Semantic Adversarial Examples with Spoofed robustness Certificates | 53 | iclr | 4 | 4 | 2023-06-18 09:10:38.986000 | https://github.com/AminJun/BreakingCertifiableDefenses | 16 | Breaking certified defenses: Semantic adversarial examples with spoofed robustness certificates | https://scholar.google.com/scholar?cluster=15252610687731481790&hl=en&as_sdt=0,33 | 5 | 2,020 |
Query-efficient Meta Attack to Deep Neural Networks | 62 | iclr | 6 | 21 | 2023-06-18 09:10:39.189000 | https://github.com/dydjw9/MetaAttack_ICLR2020 | 41 | Query-efficient meta attack to deep neural networks | https://scholar.google.com/scholar?cluster=13046330660709295854&hl=en&as_sdt=0,33 | 1 | 2,020 |
Massively Multilingual Sparse Word Representations | 1 | iclr | 1 | 0 | 2023-06-18 09:10:39.391000 | https://github.com/begab/mamus | 13 | Massively multilingual sparse word representations | https://scholar.google.com/scholar?cluster=9628937347076669673&hl=en&as_sdt=0,5 | 4 | 2,020 |
Monotonic Multihead Attention | 101 | iclr | 5,883 | 1,031 | 2023-06-18 09:10:39.595000 | https://github.com/pytorch/fairseq | 26,500 | Monotonic multihead attention | https://scholar.google.com/scholar?cluster=15976847532322302730&hl=en&as_sdt=0,19 | 411 | 2,020 |
Sparse Coding with Gated Learned ISTA | 40 | iclr | 7 | 0 | 2023-06-18 09:10:39.798000 | https://github.com/wukailun/GLISTA | 23 | Sparse coding with gated learned ISTA | https://scholar.google.com/scholar?cluster=17364655028001424684&hl=en&as_sdt=0,33 | 4 | 2,020 |
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification | 424 | iclr | 5 | 0 | 2023-06-18 09:10:40.001000 | https://github.com/delta2323/gnn-asymptotics | 30 | Graph neural networks exponentially lose expressive power for node classification | https://scholar.google.com/scholar?cluster=15290010211141332792&hl=en&as_sdt=0,33 | 4 | 2,020 |
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells | 61 | iclr | 18 | 6 | 2023-06-18 09:10:40.205000 | https://github.com/gengchenmai/space2vec | 91 | Multi-scale representation learning for spatial feature distributions using grid cells | https://scholar.google.com/scholar?cluster=5890605928845244555&hl=en&as_sdt=0,11 | 6 | 2,020 |
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization | 574 | iclr | 42 | 3 | 2023-06-18 09:10:40.408000 | https://github.com/fanyun-sun/InfoGraph | 267 | Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization | https://scholar.google.com/scholar?cluster=16670911678056840041&hl=en&as_sdt=0,26 | 7 | 2,020 |
On Robustness of Neural Ordinary Differential Equations | 114 | iclr | 4 | 0 | 2023-06-18 09:10:40.612000 | https://github.com/HanshuYAN/TisODE | 7 | On robustness of neural ordinary differential equations | https://scholar.google.com/scholar?cluster=12991236712487678100&hl=en&as_sdt=0,39 | 1 | 2,020 |
Defending Against Physically Realizable Attacks on Image Classification | 86 | iclr | 9 | 0 | 2023-06-18 09:10:40.815000 | https://github.com/tongwu2020/phattacks | 32 | Defending against physically realizable attacks on image classification | https://scholar.google.com/scholar?cluster=1916491151191652203&hl=en&as_sdt=0,32 | 2 | 2,020 |
Estimating Gradients for Discrete Random Variables by Sampling without Replacement | 48 | iclr | 6 | 0 | 2023-06-18 09:10:41.018000 | https://github.com/wouterkool/estimating-gradients-without-replacement | 36 | Estimating gradients for discrete random variables by sampling without replacement | https://scholar.google.com/scholar?cluster=8729691714489659626&hl=en&as_sdt=0,33 | 5 | 2,020 |
Learning to Control PDEs with Differentiable Physics | 126 | iclr | 135 | 2 | 2023-06-18 09:10:41.221000 | https://github.com/tum-pbs/PhiFlow | 893 | Learning to control pdes with differentiable physics | https://scholar.google.com/scholar?cluster=7687371584395325411&hl=en&as_sdt=0,18 | 22 | 2,020 |
Intensity-Free Learning of Temporal Point Processes | 100 | iclr | 26 | 3 | 2023-06-18 09:10:41.424000 | https://github.com/shchur/ifl-tpp | 63 | Intensity-free learning of temporal point processes | https://scholar.google.com/scholar?cluster=6068412872697213311&hl=en&as_sdt=0,33 | 5 | 2,020 |
A Signal Propagation Perspective for Pruning Neural Networks at Initialization | 125 | iclr | 3 | 2 | 2023-06-18 09:10:41.627000 | https://github.com/namhoonlee/spp-public | 14 | A signal propagation perspective for pruning neural networks at initialization | https://scholar.google.com/scholar?cluster=17910397385067453379&hl=en&as_sdt=0,11 | 5 | 2,020 |
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets | 209 | iclr | 9 | 1 | 2023-06-18 09:10:41.830000 | https://github.com/csdongxian/skip-connections-matter | 66 | Skip connections matter: On the transferability of adversarial examples generated with resnets | https://scholar.google.com/scholar?cluster=6211233010132912229&hl=en&as_sdt=0,44 | 4 | 2,020 |
White Noise Analysis of Neural Networks | 1,124 | iclr | 1 | 0 | 2023-06-18 09:10:42.033000 | https://github.com/aliborji/WhiteNoiseAnalysis | 13 | A simple white noise analysis of neuronal light responses | https://scholar.google.com/scholar?cluster=14064393613524789097&hl=en&as_sdt=0,33 | 2 | 2,020 |
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search | 603 | iclr | 109 | 14 | 2023-06-18 09:10:42.237000 | https://github.com/yuhuixu1993/PC-DARTS | 419 | Pc-darts: Partial channel connections for memory-efficient architecture search | https://scholar.google.com/scholar?cluster=1268458894093697275&hl=en&as_sdt=0,7 | 10 | 2,020 |
Enhancing Adversarial Defense by k-Winners-Take-All | 89 | iclr | 16 | 0 | 2023-06-18 09:10:42.440000 | https://github.com/a554b554/kWTA-Activation | 43 | Enhancing adversarial defense by k-winners-take-all | https://scholar.google.com/scholar?cluster=11915603925298453431&hl=en&as_sdt=0,33 | 3 | 2,020 |
Encoding word order in complex embeddings | 76 | iclr | 13 | 1 | 2023-06-18 09:10:42.643000 | https://github.com/iclr-complex-order/complex-order | 78 | Encoding word order in complex embeddings | https://scholar.google.com/scholar?cluster=4348415605145944586&hl=en&as_sdt=0,33 | 3 | 2,020 |
DDSP: Differentiable Digital Signal Processing | 306 | iclr | 301 | 39 | 2023-06-18 09:10:42.846000 | https://github.com/magenta/ddsp | 2,538 | DDSP: Differentiable digital signal processing | https://scholar.google.com/scholar?cluster=494865138250348922&hl=en&as_sdt=0,33 | 64 | 2,020 |
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation | 304 | iclr | 62 | 30 | 2023-06-18 09:10:43.050000 | https://github.com/hytseng0509/CrossDomainFewShot | 294 | Cross-domain few-shot classification via learned feature-wise transformation | https://scholar.google.com/scholar?cluster=7014117950265754591&hl=en&as_sdt=0,31 | 8 | 2,020 |
Ridge Regression: Structure, Cross-Validation, and Sketching | 45 | iclr | 1 | 0 | 2023-06-18 09:10:43.253000 | https://github.com/liusf15/RidgeRegression | 5 | Ridge regression: Structure, cross-validation, and sketching | https://scholar.google.com/scholar?cluster=16996813941555291674&hl=en&as_sdt=0,5 | 4 | 2,020 |
Influence-Based Multi-Agent Exploration | 82 | iclr | 5 | 3 | 2023-06-18 09:10:43.456000 | https://github.com/TonghanWang/EITI-EDTI | 25 | Influence-based multi-agent exploration | https://scholar.google.com/scholar?cluster=3107558689865611591&hl=en&as_sdt=0,18 | 3 | 2,020 |
Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs | 169 | iclr | 14 | 10 | 2023-06-18 09:10:43.659000 | https://github.com/AI-nstein/hoppity | 54 | Hoppity: Learning graph transformations to detect and fix bugs in programs | https://scholar.google.com/scholar?cluster=3537740923229776123&hl=en&as_sdt=0,10 | 6 | 2,020 |
Inductive Matrix Completion Based on Graph Neural Networks | 193 | iclr | 80 | 5 | 2023-06-18 09:10:43.862000 | https://github.com/muhanzhang/IGMC | 330 | Inductive matrix completion based on graph neural networks | https://scholar.google.com/scholar?cluster=16467785209736673104&hl=en&as_sdt=0,5 | 13 | 2,020 |
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations | 4,946 | iclr | 559 | 101 | 2023-06-18 09:10:44.065000 | https://github.com/google-research/ALBERT | 3,115 | Albert: A lite bert for self-supervised learning of language representations | https://scholar.google.com/scholar?cluster=6606720413006378435&hl=en&as_sdt=0,10 | 75 | 2,020 |
Symplectic Recurrent Neural Networks | 173 | iclr | 8 | 1 | 2023-06-18 09:10:44.273000 | https://github.com/zhengdao-chen/SRNN | 25 | Symplectic recurrent neural networks | https://scholar.google.com/scholar?cluster=16381042632484621201&hl=en&as_sdt=0,33 | 4 | 2,020 |
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning | 71 | iclr | 37 | 12 | 2023-06-18 09:10:44.478000 | https://github.com/facebookresearch/Hanabi_SAD | 90 | Simplified action decoder for deep multi-agent reinforcement learning | https://scholar.google.com/scholar?cluster=17934059469747464722&hl=en&as_sdt=0,33 | 11 | 2,020 |
Real or Not Real, that is the Question | 34 | iclr | 39 | 6 | 2023-06-18 09:10:44.682000 | https://github.com/kam1107/RealnessGAN | 285 | Real or not real, that is the question | https://scholar.google.com/scholar?cluster=1314869860103528088&hl=en&as_sdt=0,33 | 7 | 2,020 |
Dream to Control: Learning Behaviors by Latent Imagination | 768 | iclr | 100 | 4 | 2023-06-18 09:10:44.885000 | https://github.com/danijar/dreamer | 446 | Dream to control: Learning behaviors by latent imagination | https://scholar.google.com/scholar?cluster=14974700822970491825&hl=en&as_sdt=0,33 | 11 | 2,020 |
A Probabilistic Formulation of Unsupervised Text Style Transfer | 102 | iclr | 25 | 5 | 2023-06-18 09:10:45.098000 | https://github.com/cindyxinyiwang/deep-latent-sequence-model | 160 | A probabilistic formulation of unsupervised text style transfer | https://scholar.google.com/scholar?cluster=12354733292674478284&hl=en&as_sdt=0,34 | 7 | 2,020 |
Emergent Tool Use From Multi-Agent Autocurricula | 597 | iclr | 290 | 26 | 2023-06-18 09:10:45.312000 | https://github.com/openai/multi-agent-emergence-environments | 1,471 | Emergent tool use from multi-agent autocurricula | https://scholar.google.com/scholar?cluster=428666358348789864&hl=en&as_sdt=0,33 | 167 | 2,020 |
Behaviour Suite for Reinforcement Learning | 130 | iclr | 179 | 16 | 2023-06-18 09:10:45.514000 | https://github.com/deepmind/bsuite | 1,400 | Behaviour suite for reinforcement learning | https://scholar.google.com/scholar?cluster=10471200174222163517&hl=en&as_sdt=0,5 | 62 | 2,020 |
FreeLB: Enhanced Adversarial Training for Natural Language Understanding | 332 | iclr | 38 | 4 | 2023-06-18 09:10:45.717000 | https://github.com/zhuchen03/FreeLB | 242 | Freelb: Enhanced adversarial training for natural language understanding | https://scholar.google.com/scholar?cluster=18174532754984286160&hl=en&as_sdt=0,21 | 9 | 2,020 |
Kernelized Wasserstein Natural Gradient | 16 | iclr | 2 | 1 | 2023-06-18 09:10:45.921000 | https://github.com/MichaelArbel/KWNG | 12 | Kernelized wasserstein natural gradient | https://scholar.google.com/scholar?cluster=4819202851249905644&hl=en&as_sdt=0,1 | 2 | 2,020 |
And the Bit Goes Down: Revisiting the Quantization of Neural Networks | 131 | iclr | 128 | 0 | 2023-06-18 09:10:46.123000 | https://github.com/facebookresearch/kill-the-bits | 628 | And the bit goes down: Revisiting the quantization of neural networks | https://scholar.google.com/scholar?cluster=9220174723943814446&hl=en&as_sdt=0,46 | 25 | 2,020 |
A Latent Morphology Model for Open-Vocabulary Neural Machine Translation | 19 | iclr | 1 | 0 | 2023-06-18 09:10:46.354000 | https://github.com/d-ataman/lmm | 8 | A latent morphology model for open-vocabulary neural machine translation | https://scholar.google.com/scholar?cluster=9869395538651177404&hl=en&as_sdt=0,18 | 2 | 2,020 |
Disagreement-Regularized Imitation Learning | 77 | iclr | 11 | 0 | 2023-06-18 09:10:46.557000 | https://github.com/xkianteb/dril | 27 | Disagreement-regularized imitation learning | https://scholar.google.com/scholar?cluster=11799935294964766757&hl=en&as_sdt=0,5 | 3 | 2,020 |
Measuring the Reliability of Reinforcement Learning Algorithms | 59 | iclr | 20 | 0 | 2023-06-18 09:10:46.760000 | https://github.com/google-research/rl-reliability-metrics | 143 | Measuring the reliability of reinforcement learning algorithms | https://scholar.google.com/scholar?cluster=921553679446510240&hl=en&as_sdt=0,5 | 11 | 2,020 |