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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 |
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