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Dynamic Time Lag Regression: Predicting What & When
| 9 |
iclr
| 1 | 7 |
2023-06-18 09:10:06.100000
|
https://github.com/transcendent-ai-labs/PlasmaML
| 16 |
Dynamic Time Lag Regression: Predicting What and When
|
https://scholar.google.com/scholar?cluster=5170552035479326246&hl=en&as_sdt=0,37
| 7 | 2,020 |
Unpaired Point Cloud Completion on Real Scans using Adversarial Training
| 96 |
iclr
| 11 | 1 |
2023-06-18 09:10:06.302000
|
https://github.com/xuelin-chen/pcl2pcl-gan-pub
| 81 |
Unpaired point cloud completion on real scans using adversarial training
|
https://scholar.google.com/scholar?cluster=6319477762897752803&hl=en&as_sdt=0,5
| 7 | 2,020 |
Selection via Proxy: Efficient Data Selection for Deep Learning
| 133 |
iclr
| 19 | 1 |
2023-06-18 09:10:06.506000
|
https://github.com/stanford-futuredata/selection-via-proxy
| 78 |
Selection via proxy: Efficient data selection for deep learning
|
https://scholar.google.com/scholar?cluster=10606664093807319412&hl=en&as_sdt=0,32
| 8 | 2,020 |
Global Relational Models of Source Code
| 194 |
iclr
| 20 | 3 |
2023-06-18 09:10:06.708000
|
https://github.com/VHellendoorn/ICLR20-Great
| 79 |
Global relational models of source code
|
https://scholar.google.com/scholar?cluster=5949441341653621917&hl=en&as_sdt=0,5
| 4 | 2,020 |
Adversarially robust transfer learning
| 103 |
iclr
| 2 | 1 |
2023-06-18 09:10:06.912000
|
https://github.com/ashafahi/RobustTransferLWF
| 16 |
Adversarially robust transfer learning
|
https://scholar.google.com/scholar?cluster=247907928453605112&hl=en&as_sdt=0,47
| 4 | 2,020 |
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
| 119 |
iclr
| 6 | 1 |
2023-06-18 09:10:07.115000
|
https://github.com/IBM/model-sanitization
| 22 |
Bridging mode connectivity in loss landscapes and adversarial robustness
|
https://scholar.google.com/scholar?cluster=14988732432147772285&hl=en&as_sdt=0,33
| 7 | 2,020 |
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
| 491 |
iclr
| 136 | 44 |
2023-06-18 09:10:07.317000
|
https://github.com/google-research/meta-dataset
| 698 |
Meta-dataset: A dataset of datasets for learning to learn from few examples
|
https://scholar.google.com/scholar?cluster=14266702502378757393&hl=en&as_sdt=0,32
| 24 | 2,020 |
Deep Imitative Models for Flexible Inference, Planning, and Control
| 124 |
iclr
| 14 | 19 |
2023-06-18 09:10:07.521000
|
https://github.com/nrhine1/deep_imitative_models
| 68 |
Deep imitative models for flexible inference, planning, and control
|
https://scholar.google.com/scholar?cluster=599185864570432210&hl=en&as_sdt=0,45
| 3 | 2,020 |
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
| 75 |
iclr
| 10 | 0 |
2023-06-18 09:10:07.723000
|
https://github.com/011235813/cm3
| 47 |
Cm3: Cooperative multi-goal multi-stage multi-agent reinforcement learning
|
https://scholar.google.com/scholar?cluster=11188676090053014781&hl=en&as_sdt=0,3
| 3 | 2,020 |
Robust And Interpretable Blind Image Denoising Via Bias-Free Convolutional Neural Networks
| 84 |
iclr
| 9 | 3 |
2023-06-18 09:10:07.925000
|
https://github.com/LabForComputationalVision/bias_free_denoising
| 36 |
Robust and interpretable blind image denoising via bias-free convolutional neural networks
|
https://scholar.google.com/scholar?cluster=11707547899272178627&hl=en&as_sdt=0,36
| 5 | 2,020 |
DeepV2D: Video to Depth with Differentiable Structure from Motion
| 146 |
iclr
| 89 | 28 |
2023-06-18 09:10:08.128000
|
https://github.com/princeton-vl/DeepV2D
| 598 |
Deepv2d: Video to depth with differentiable structure from motion
|
https://scholar.google.com/scholar?cluster=564045569449021652&hl=en&as_sdt=0,33
| 20 | 2,020 |
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
| 142 |
iclr
| 27 | 10 |
2023-06-18 09:10:08.331000
|
https://github.com/cmhcbb/attackbox
| 50 |
Sign-opt: A query-efficient hard-label adversarial attack
|
https://scholar.google.com/scholar?cluster=4337120578340154737&hl=en&as_sdt=0,5
| 5 | 2,020 |
Fast is better than free: Revisiting adversarial training
| 869 |
iclr
| 92 | 2 |
2023-06-18 09:10:08.534000
|
https://github.com/locuslab/fast_adversarial
| 385 |
Fast is better than free: Revisiting adversarial training
|
https://scholar.google.com/scholar?cluster=227717459026762223&hl=en&as_sdt=0,6
| 12 | 2,020 |
DBA: Distributed Backdoor Attacks against Federated Learning
| 377 |
iclr
| 37 | 4 |
2023-06-18 09:10:08.737000
|
https://github.com/AI-secure/DBA
| 134 |
Dba: Distributed backdoor attacks against federated learning
|
https://scholar.google.com/scholar?cluster=12314378493827075057&hl=en&as_sdt=0,1
| 2 | 2,020 |
DeFINE: Deep Factorized Input Token Embeddings for Neural Sequence Modeling
| 19 |
iclr
| 50 | 7 |
2023-06-18 09:10:08.941000
|
https://github.com/sacmehta/delight
| 443 |
Define: Deep factorized input token embeddings for neural sequence modeling
|
https://scholar.google.com/scholar?cluster=1535018014104631427&hl=en&as_sdt=0,29
| 14 | 2,020 |
Learning to solve the credit assignment problem
| 53 |
iclr
| 0 | 0 |
2023-06-18 09:10:09.143000
|
https://github.com/benlansdell/synthfeedback
| 3 |
Learning to solve the credit assignment problem
|
https://scholar.google.com/scholar?cluster=1954938718512669715&hl=en&as_sdt=0,37
| 5 | 2,020 |
Four Things Everyone Should Know to Improve Batch Normalization
| 48 |
iclr
| 1 | 1 |
2023-06-18 09:10:09.347000
|
https://github.com/ceciliaresearch/four_things_batch_norm
| 20 |
Four things everyone should know to improve batch normalization
|
https://scholar.google.com/scholar?cluster=8831824515210942226&hl=en&as_sdt=0,5
| 1 | 2,020 |
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
| 312 |
iclr
| 116 | 18 |
2023-06-18 09:10:09.551000
|
https://github.com/mileyan/Pseudo_Lidar_V2
| 539 |
Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving
|
https://scholar.google.com/scholar?cluster=10904480408184954283&hl=en&as_sdt=0,10
| 40 | 2,020 |
Learning to Learn by Zeroth-Order Oracle
| 14 |
iclr
| 5 | 0 |
2023-06-18 09:10:09.753000
|
https://github.com/RYoungJ/ZO-L2L
| 13 |
Learning to learn by zeroth-order oracle
|
https://scholar.google.com/scholar?cluster=8954748594282159172&hl=en&as_sdt=0,31
| 2 | 2,020 |
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
| 273 |
iclr
| 378 | 170 |
2023-06-18 09:10:09.955000
|
https://github.com/facebookresearch/habitat-api
| 1,109 |
Dd-ppo: Learning near-perfect pointgoal navigators from 2.5 billion frames
|
https://scholar.google.com/scholar?cluster=4884965845219755657&hl=en&as_sdt=0,6
| 43 | 2,020 |
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
| 38 |
iclr
| 1 | 0 |
2023-06-18 09:10:10.159000
|
https://github.com/sangdon/PAC-confidence-set
| 5 |
PAC confidence sets for deep neural networks via calibrated prediction
|
https://scholar.google.com/scholar?cluster=13464804698510313899&hl=en&as_sdt=0,5
| 2 | 2,020 |
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations
| 19 |
iclr
| 12 | 3 |
2023-06-18 09:10:10.362000
|
https://github.com/cornell-zhang/dnn-gating
| 69 |
Precision gating: Improving neural network efficiency with dynamic dual-precision activations
|
https://scholar.google.com/scholar?cluster=5604094105865350488&hl=en&as_sdt=0,39
| 9 | 2,020 |
Oblique Decision Trees from Derivatives of ReLU Networks
| 12 |
iclr
| 7 | 1 |
2023-06-18 09:10:10.564000
|
https://github.com/guanghelee/iclr20-lcn
| 20 |
Oblique decision trees from derivatives of relu networks
|
https://scholar.google.com/scholar?cluster=15458108821420666095&hl=en&as_sdt=0,31
| 4 | 2,020 |
Learn to Explain Efficiently via Neural Logic Inductive Learning
| 58 |
iclr
| 17 | 3 |
2023-06-18 09:10:10.768000
|
https://github.com/gblackout/NLIL
| 38 |
Learn to explain efficiently via neural logic inductive learning
|
https://scholar.google.com/scholar?cluster=4550874980727321525&hl=en&as_sdt=0,15
| 4 | 2,020 |
Improved memory in recurrent neural networks with sequential non-normal dynamics
| 12 |
iclr
| 2 | 0 |
2023-06-18 09:10:10.971000
|
https://github.com/eminorhan/nonnormal-init
| 3 |
Improved memory in recurrent neural networks with sequential non-normal dynamics
|
https://scholar.google.com/scholar?cluster=2472327505855554396&hl=en&as_sdt=0,26
| 3 | 2,020 |
Neural Module Networks for Reasoning over Text
| 121 |
iclr
| 14 | 3 |
2023-06-18 09:10:11.174000
|
https://github.com/nitishgupta/nmn-drop
| 120 |
Neural module networks for reasoning over text
|
https://scholar.google.com/scholar?cluster=2046532742306416986&hl=en&as_sdt=0,5
| 11 | 2,020 |
Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling
| 2 |
iclr
| 1 | 1 |
2023-06-18 09:10:11.377000
|
https://github.com/BoChenGroup/VHE-GAN
| 9 |
Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling
|
https://scholar.google.com/scholar?cluster=6283375856940214417&hl=en&as_sdt=0,5
| 2 | 2,020 |
Towards Fast Adaptation of Neural Architectures with Meta Learning
| 70 |
iclr
| 7 | 2 |
2023-06-18 09:10:11.580000
|
https://github.com/dongzelian/T-NAS
| 27 |
Towards fast adaptation of neural architectures with meta learning
|
https://scholar.google.com/scholar?cluster=2375275580093901945&hl=en&as_sdt=0,5
| 3 | 2,020 |
Graph Constrained Reinforcement Learning for Natural Language Action Spaces
| 83 |
iclr
| 13 | 1 |
2023-06-18 09:10:11.783000
|
https://github.com/rajammanabrolu/KG-A2C
| 54 |
Graph constrained reinforcement learning for natural language action spaces
|
https://scholar.google.com/scholar?cluster=15066208654437399788&hl=en&as_sdt=0,5
| 2 | 2,020 |
BERTScore: Evaluating Text Generation with BERT
| 2,078 |
iclr
| 186 | 12 |
2023-06-18 09:10:11.986000
|
https://github.com/Tiiiger/bert_score
| 1,161 |
Bertscore: Evaluating text generation with bert
|
https://scholar.google.com/scholar?cluster=5304773001741994283&hl=en&as_sdt=0,5
| 22 | 2,020 |
Composition-based Multi-Relational Graph Convolutional Networks
| 533 |
iclr
| 102 | 13 |
2023-06-18 09:10:12.190000
|
https://github.com/malllabiisc/CompGCN
| 545 |
Composition-based multi-relational graph convolutional networks
|
https://scholar.google.com/scholar?cluster=4927480689371858635&hl=en&as_sdt=0,5
| 17 | 2,020 |
Gradient-Based Neural DAG Learning
| 150 |
iclr
| 19 | 2 |
2023-06-18 09:10:12.393000
|
https://github.com/kurowasan/GraN-DAG
| 78 |
Gradient-based neural dag learning
|
https://scholar.google.com/scholar?cluster=10487378596908501013&hl=en&as_sdt=0,10
| 6 | 2,020 |
The Local Elasticity of Neural Networks
| 29 |
iclr
| 2 | 1 |
2023-06-18 09:10:12.596000
|
https://github.com/HornHehhf/LocalElasticity
| 6 |
The local elasticity of neural networks
|
https://scholar.google.com/scholar?cluster=2497659647078092985&hl=en&as_sdt=0,38
| 3 | 2,020 |
Convergence of Gradient Methods on Bilinear Zero-Sum Games
| 33 |
iclr
| 1 | 0 |
2023-06-18 09:10:12.799000
|
https://github.com/Gordon-Guojun-Zhang/ICLR-2020
| 1 |
Convergence of gradient methods on bilinear zero-sum games
|
https://scholar.google.com/scholar?cluster=18092221422699658079&hl=en&as_sdt=0,31
| 2 | 2,020 |
Learning from Explanations with Neural Execution Tree
| 33 |
iclr
| 4 | 0 |
2023-06-18 09:10:13.002000
|
https://github.com/INK-USC/NExT
| 18 |
Learning from explanations with neural execution tree
|
https://scholar.google.com/scholar?cluster=7878469874238216625&hl=en&as_sdt=0,5
| 6 | 2,020 |
Jelly Bean World: A Testbed for Never-Ending Learning
| 19 |
iclr
| 14 | 2 |
2023-06-18 09:10:13.205000
|
https://github.com/eaplatanios/jelly-bean-world
| 68 |
Jelly bean world: A testbed for never-ending learning
|
https://scholar.google.com/scholar?cluster=13920710483001851413&hl=en&as_sdt=0,5
| 6 | 2,020 |
Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality
| 32 |
iclr
| 3 | 0 |
2023-06-18 09:10:13.408000
|
https://github.com/sakhanna/SRU_for_GCI
| 21 |
Economy statistical recurrent units for inferring nonlinear granger causality
|
https://scholar.google.com/scholar?cluster=9739971127623592335&hl=en&as_sdt=0,14
| 2 | 2,020 |
Bayesian Meta Sampling for Fast Uncertainty Adaptation
| 17 |
iclr
| 3 | 0 |
2023-06-18 09:10:13.611000
|
https://github.com/zheshiyige/meta-sampling
| 8 |
Bayesian meta sampling for fast uncertainty adaptation
|
https://scholar.google.com/scholar?cluster=15645160927746258341&hl=en&as_sdt=0,5
| 1 | 2,020 |
Non-Autoregressive Dialog State Tracking
| 49 |
iclr
| 3 | 2 |
2023-06-18 09:10:13.814000
|
https://github.com/henryhungle/NADST
| 45 |
Non-autoregressive dialog state tracking
|
https://scholar.google.com/scholar?cluster=13522465904465807685&hl=en&as_sdt=0,5
| 5 | 2,020 |
RNNs Incrementally Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients?
| 40 |
iclr
| 1 | 0 |
2023-06-18 09:10:14.018000
|
https://github.com/anilkagak2/TARNN
| 6 |
Rnns incrementally evolving on an equilibrium manifold: A panacea for vanishing and exploding gradients?
|
https://scholar.google.com/scholar?cluster=14548762609337726303&hl=en&as_sdt=0,5
| 3 | 2,020 |
The Early Phase of Neural Network Training
| 128 |
iclr
| 106 | 15 |
2023-06-18 09:10:14.220000
|
https://github.com/facebookresearch/open_lth
| 590 |
The early phase of neural network training
|
https://scholar.google.com/scholar?cluster=15707294236176535435&hl=en&as_sdt=0,5
| 57 | 2,020 |
Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization
| 37 |
iclr
| 25 | 0 |
2023-06-18 09:10:14.424000
|
https://github.com/megvii-model/MABN
| 182 |
Towards stabilizing batch statistics in backward propagation of batch normalization
|
https://scholar.google.com/scholar?cluster=2467606863922912536&hl=en&as_sdt=0,5
| 8 | 2,020 |
Single Episode Policy Transfer in Reinforcement Learning
| 27 |
iclr
| 3 | 0 |
2023-06-18 09:10:14.627000
|
https://github.com/011235813/SEPT
| 16 |
Single episode policy transfer in reinforcement learning
|
https://scholar.google.com/scholar?cluster=2255040216539653326&hl=en&as_sdt=0,14
| 5 | 2,020 |
Generalization through Memorization: Nearest Neighbor Language Models
| 360 |
iclr
| 41 | 4 |
2023-06-18 09:10:14.830000
|
https://github.com/urvashik/knnlm
| 253 |
Generalization through memorization: Nearest neighbor language models
|
https://scholar.google.com/scholar?cluster=17433739628027955410&hl=en&as_sdt=0,5
| 7 | 2,020 |
Transformer-XH: Multi-Evidence Reasoning with eXtra Hop Attention
| 98 |
iclr
| 15 | 1 |
2023-06-18 09:10:15.034000
|
https://github.com/microsoft/Transformer-XH
| 67 |
Transformer-xh: Multi-evidence reasoning with extra hop attention
|
https://scholar.google.com/scholar?cluster=1330946954324829338&hl=en&as_sdt=0,5
| 8 | 2,020 |
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks
| 56 |
iclr
| 12 | 0 |
2023-06-18 09:10:15.236000
|
https://github.com/facebookresearch/GAN-optimization-landscape
| 31 |
A closer look at the optimization landscapes of generative adversarial networks
|
https://scholar.google.com/scholar?cluster=8697338348379515621&hl=en&as_sdt=0,3
| 6 | 2,020 |
Revisiting Self-Training for Neural Sequence Generation
| 191 |
iclr
| 8 | 2 |
2023-06-18 09:10:15.440000
|
https://github.com/jxhe/self-training-text-generation
| 45 |
Revisiting self-training for neural sequence generation
|
https://scholar.google.com/scholar?cluster=7004703497998979134&hl=en&as_sdt=0,47
| 2 | 2,020 |
Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators
| 58 |
iclr
| 4 | 0 |
2023-06-18 09:10:15.643000
|
https://github.com/MLI-lab/overparameterized_convolutional_generators
| 14 |
Denoising and regularization via exploiting the structural bias of convolutional generators
|
https://scholar.google.com/scholar?cluster=11773092557321050875&hl=en&as_sdt=0,5
| 4 | 2,020 |
LambdaNet: Probabilistic Type Inference using Graph Neural Networks
| 88 |
iclr
| 12 | 0 |
2023-06-18 09:10:15.846000
|
https://github.com/MrVPlusOne/LambdaNet
| 42 |
Lambdanet: Probabilistic type inference using graph neural networks
|
https://scholar.google.com/scholar?cluster=14484091760382594314&hl=en&as_sdt=0,5
| 9 | 2,020 |
Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping
| 22 |
iclr
| 4 | 0 |
2023-06-18 09:10:16.050000
|
https://github.com/aharley/neural_3d_mapping
| 31 |
Learning from unlabelled videos using contrastive predictive neural 3d mapping
|
https://scholar.google.com/scholar?cluster=7365572649342061474&hl=en&as_sdt=0,33
| 8 | 2,020 |
Decoupling Representation and Classifier for Long-Tailed Recognition
| 786 |
iclr
| 117 | 13 |
2023-06-18 09:10:16.279000
|
https://github.com/facebookresearch/classifier-balancing
| 873 |
Decoupling representation and classifier for long-tailed recognition
|
https://scholar.google.com/scholar?cluster=2236026226436038230&hl=en&as_sdt=0,41
| 21 | 2,020 |
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework
| 61 |
iclr
| 10 | 1 |
2023-06-18 09:10:16.482000
|
https://github.com/thespectrewithin/joint-align
| 51 |
Cross-lingual alignment vs joint training: A comparative study and a simple unified framework
|
https://scholar.google.com/scholar?cluster=17808816563200033029&hl=en&as_sdt=0,33
| 4 | 2,020 |
Uncertainty-guided Continual Learning with Bayesian Neural Networks
| 165 |
iclr
| 11 | 8 |
2023-06-18 09:10:16.686000
|
https://github.com/SaynaEbrahimi/UCB
| 66 |
Uncertainty-guided continual learning with bayesian neural networks
|
https://scholar.google.com/scholar?cluster=10082473234430355613&hl=en&as_sdt=0,39
| 4 | 2,020 |
Picking Winning Tickets Before Training by Preserving Gradient Flow
| 378 |
iclr
| 11 | 1 |
2023-06-18 09:10:16.889000
|
https://github.com/alecwangcq/GraSP
| 91 |
Picking winning tickets before training by preserving gradient flow
|
https://scholar.google.com/scholar?cluster=9466463567127487961&hl=en&as_sdt=0,10
| 2 | 2,020 |
Inductive representation learning on temporal graphs
| 299 |
iclr
| 53 | 12 |
2023-06-18 09:10:17.092000
|
https://github.com/StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs
| 222 |
Inductive representation learning on temporal graphs
|
https://scholar.google.com/scholar?cluster=6732351798905235278&hl=en&as_sdt=0,36
| 3 | 2,020 |
BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning
| 287 |
iclr
| 79 | 73 |
2023-06-18 09:10:17.295000
|
https://github.com/google/edward2
| 645 |
Batchensemble: an alternative approach to efficient ensemble and lifelong learning
|
https://scholar.google.com/scholar?cluster=2684475579133602&hl=en&as_sdt=0,21
| 20 | 2,020 |
Towards neural networks that provably know when they don't know
| 121 |
iclr
| 1 | 1 |
2023-06-18 09:10:17.498000
|
https://github.com/AlexMeinke/certified-certain-uncertainty
| 34 |
Towards neural networks that provably know when they don't know
|
https://scholar.google.com/scholar?cluster=3907037768613550224&hl=en&as_sdt=0,5
| 5 | 2,020 |
Learning representations for binary-classification without backpropagation
| 7 |
iclr
| 2 | 0 |
2023-06-18 09:10:17.702000
|
https://github.com/mlech26l/iclr_paper_mdfa
| 2 |
Learning representations for binary-classification without backpropagation
|
https://scholar.google.com/scholar?cluster=6618144182532521283&hl=en&as_sdt=0,34
| 2 | 2,020 |
HiLLoC: lossless image compression with hierarchical latent variable models
| 51 |
iclr
| 7 | 1 |
2023-06-18 09:10:17.915000
|
https://github.com/hilloc-submission/hilloc
| 34 |
Hilloc: Lossless image compression with hierarchical latent variable models
|
https://scholar.google.com/scholar?cluster=8743808448385898182&hl=en&as_sdt=0,36
| 7 | 2,020 |
Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation
| 3 |
iclr
| 3 | 0 |
2023-06-18 09:10:18.119000
|
https://github.com/xinjiefan/ACMC_ICLR
| 4 |
Adaptive correlated Monte Carlo for contextual categorical sequence generation
|
https://scholar.google.com/scholar?cluster=3786399280246105812&hl=en&as_sdt=0,15
| 4 | 2,020 |
PairNorm: Tackling Oversmoothing in GNNs
| 371 |
iclr
| 11 | 4 |
2023-06-18 09:10:18.321000
|
https://github.com/LingxiaoShawn/PairNorm
| 68 |
Pairnorm: Tackling oversmoothing in gnns
|
https://scholar.google.com/scholar?cluster=244277682967965047&hl=en&as_sdt=0,5
| 2 | 2,020 |
Controlling generative models with continuous factors of variations
| 104 |
iclr
| 4 | 8 |
2023-06-18 09:10:18.524000
|
https://github.com/AntoinePlumerault/Controlling-generative-models-with-continuous-factors-of-variations
| 20 |
Controlling generative models with continuous factors of variations
|
https://scholar.google.com/scholar?cluster=9062279682169095695&hl=en&as_sdt=0,5
| 2 | 2,020 |
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control
| 211 |
iclr
| 12 | 0 |
2023-06-18 09:10:18.727000
|
https://github.com/Physics-aware-AI/Symplectic-ODENet
| 34 |
Symplectic ode-net: Learning hamiltonian dynamics with control
|
https://scholar.google.com/scholar?cluster=16212087481734650197&hl=en&as_sdt=0,33
| 5 | 2,020 |
Quantum Algorithms for Deep Convolutional Neural Networks
| 103 |
iclr
| 15 | 1 |
2023-06-18 09:10:18.929000
|
https://github.com/JonasLandman/QCNN
| 84 |
Quantum algorithms for deep convolutional neural networks
|
https://scholar.google.com/scholar?cluster=6858802029383173289&hl=en&as_sdt=0,10
| 1 | 2,020 |
Deep Graph Matching Consensus
| 175 |
iclr
| 45 | 4 |
2023-06-18 09:10:19.132000
|
https://github.com/rusty1s/deep-graph-matching-consensus
| 238 |
Deep graph matching consensus
|
https://scholar.google.com/scholar?cluster=13831077548402480322&hl=en&as_sdt=0,33
| 9 | 2,020 |
Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers
| 77 |
iclr
| 5 | 1 |
2023-06-18 09:10:19.336000
|
https://github.com/junjieliu2910/DynamicSaprseTraining
| 27 |
Dynamic sparse training: Find efficient sparse network from scratch with trainable masked layers
|
https://scholar.google.com/scholar?cluster=2417069645139449524&hl=en&as_sdt=0,5
| 3 | 2,020 |
Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference
| 72 |
iclr
| 7 | 1 |
2023-06-18 09:10:19.538000
|
https://github.com/TAMU-VITA/triple-wins
| 22 |
Triple wins: Boosting accuracy, robustness and efficiency together by enabling input-adaptive inference
|
https://scholar.google.com/scholar?cluster=16965650260059633977&hl=en&as_sdt=0,33
| 12 | 2,020 |
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
| 250 |
iclr
| 4 | 1 |
2023-06-18 09:10:19.741000
|
https://github.com/DeepGraphLearning/GraphAF
| 44 |
Graphaf: a flow-based autoregressive model for molecular graph generation
|
https://scholar.google.com/scholar?cluster=2901334410635777038&hl=en&as_sdt=0,19
| 8 | 2,020 |
The Curious Case of Neural Text Degeneration
| 1,564 |
iclr
| 13 | 2 |
2023-06-18 09:10:19.943000
|
https://github.com/ari-holtzman/degen
| 131 |
The curious case of neural text degeneration
|
https://scholar.google.com/scholar?cluster=13091440005032798110&hl=en&as_sdt=0,33
| 5 | 2,020 |
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning
| 85 |
iclr
| 1 | 1 |
2023-06-18 09:10:20.146000
|
https://github.com/KDL-umass/saliency_maps
| 9 |
Exploratory not explanatory: Counterfactual analysis of saliency maps for deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=6988064126122361563&hl=en&as_sdt=0,5
| 6 | 2,020 |
Guiding Program Synthesis by Learning to Generate Examples
| 12 |
iclr
| 3 | 1 |
2023-06-18 09:10:20.349000
|
https://github.com/eth-sri/guiding-synthesizers
| 12 |
Guiding program synthesis by learning to generate examples
|
https://scholar.google.com/scholar?cluster=5759998545534932408&hl=en&as_sdt=0,14
| 9 | 2,020 |
Once-for-All: Train One Network and Specialize it for Efficient Deployment
| 930 |
iclr
| 309 | 55 |
2023-06-18 09:10:20.553000
|
https://github.com/mit-han-lab/once-for-all
| 1,676 |
Once-for-all: Train one network and specialize it for efficient deployment
|
https://scholar.google.com/scholar?cluster=5004054402916064925&hl=en&as_sdt=0,47
| 53 | 2,020 |
Multi-Agent Interactions Modeling with Correlated Policies
| 14 |
iclr
| 1 | 0 |
2023-06-18 09:10:20.755000
|
https://github.com/apexrl/CoDAIL
| 19 |
Multi-agent interactions modeling with correlated policies
|
https://scholar.google.com/scholar?cluster=1707555896923900607&hl=en&as_sdt=0,11
| 4 | 2,020 |
PCMC-Net: Feature-based Pairwise Choice Markov Chains
| 4 |
iclr
| 2 | 0 |
2023-06-18 09:10:20.958000
|
https://github.com/alherit/PCMC-Net
| 0 |
PCMC-Net: Feature-based pairwise choice Markov chains
|
https://scholar.google.com/scholar?cluster=6364308783173808929&hl=en&as_sdt=0,5
| 2 | 2,020 |
Query2box: Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings
| 192 |
iclr
| 25 | 4 |
2023-06-18 09:10:21.161000
|
https://github.com/hyren/query2box
| 185 |
Query2box: Reasoning over knowledge graphs in vector space using box embeddings
|
https://scholar.google.com/scholar?cluster=12162114509339906104&hl=en&as_sdt=0,23
| 5 | 2,020 |
Rethinking the Hyperparameters for Fine-tuning
| 91 |
iclr
| 35 | 8 |
2023-06-18 09:10:21.364000
|
https://github.com/richardaecn/cvpr18-inaturalist-transfer
| 189 |
Rethinking the hyperparameters for fine-tuning
|
https://scholar.google.com/scholar?cluster=14029720773108023404&hl=en&as_sdt=0,44
| 9 | 2,020 |
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
| 532 |
iclr
| 187 | 26 |
2023-06-18 09:10:21.567000
|
https://github.com/uber-research/PPLM
| 1,061 |
Plug and play language models: A simple approach to controlled text generation
|
https://scholar.google.com/scholar?cluster=9850887597524341216&hl=en&as_sdt=0,5
| 29 | 2,020 |
Jacobian Adversarially Regularized Networks for Robustness
| 59 |
iclr
| 0 | 2 |
2023-06-18 09:10:21.769000
|
https://github.com/alvinchangw/JARN_ICLR2020
| 20 |
Jacobian adversarially regularized networks for robustness
|
https://scholar.google.com/scholar?cluster=8296271536774350168&hl=en&as_sdt=0,5
| 3 | 2,020 |
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning
| 69 |
iclr
| 24 | 15 |
2023-06-18 09:10:21.972000
|
https://github.com/qian18long/epciclr2020
| 103 |
Evolutionary population curriculum for scaling multi-agent reinforcement learning
|
https://scholar.google.com/scholar?cluster=13227492821855003720&hl=en&as_sdt=0,5
| 6 | 2,020 |
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
| 2,536 |
iclr
| 339 | 58 |
2023-06-18 09:10:22.175000
|
https://github.com/google-research/electra
| 2,195 |
Electra: Pre-training text encoders as discriminators rather than generators
|
https://scholar.google.com/scholar?cluster=18273102803868155691&hl=en&as_sdt=0,22
| 61 | 2,020 |
Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering
| 245 |
iclr
| 66 | 2 |
2023-06-18 09:10:22.382000
|
https://github.com/AkariAsai/learning_to_retrieve_reasoning_paths
| 409 |
Learning to retrieve reasoning paths over wikipedia graph for question answering
|
https://scholar.google.com/scholar?cluster=9983656712986759365&hl=en&as_sdt=0,5
| 18 | 2,020 |
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks
| 51 |
iclr
| 7 | 2 |
2023-06-18 09:10:22.585000
|
https://github.com/ml-research/pau
| 53 |
Pad\'e Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks
|
https://scholar.google.com/scholar?cluster=10060434819073628670&hl=en&as_sdt=0,5
| 6 | 2,020 |
Contrastive Representation Distillation
| 731 |
iclr
| 352 | 34 |
2023-06-18 09:10:22.788000
|
https://github.com/HobbitLong/RepDistiller
| 1,829 |
Contrastive representation distillation
|
https://scholar.google.com/scholar?cluster=11598873002614112751&hl=en&as_sdt=0,33
| 17 | 2,020 |
Certified Defenses for Adversarial Patches
| 120 |
iclr
| 3 | 0 |
2023-06-18 09:10:22.992000
|
https://github.com/Ping-C/certifiedpatchdefense
| 30 |
Certified defenses for adversarial patches
|
https://scholar.google.com/scholar?cluster=2964763599882748614&hl=en&as_sdt=0,5
| 2 | 2,020 |
Deep Symbolic Superoptimization Without Human Knowledge
| 4 |
iclr
| 1 | 2 |
2023-06-18 09:10:23.195000
|
https://github.com/shihui2010/symbolic_simplifier
| 14 |
Deep symbolic superoptimization without human knowledge
|
https://scholar.google.com/scholar?cluster=1299108471437991049&hl=en&as_sdt=0,33
| 4 | 2,020 |
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution
| 56 |
iclr
| 1 | 4 |
2023-06-18 09:10:23.399000
|
https://github.com/rl-interpretation/understandingRL
| 4 |
Explain your move: Understanding agent actions using specific and relevant feature attribution
|
https://scholar.google.com/scholar?cluster=5830219427979176885&hl=en&as_sdt=0,5
| 0 | 2,020 |
Universal Approximation with Certified Networks
| 19 |
iclr
| 0 | 0 |
2023-06-18 09:10:23.601000
|
https://github.com/eth-sri/UniversalCertificationTheory
| 10 |
Universal approximation with certified networks
|
https://scholar.google.com/scholar?cluster=8301791316229019028&hl=en&as_sdt=0,21
| 8 | 2,020 |
Measuring and Improving the Use of Graph Information in Graph Neural Networks
| 101 |
iclr
| 10 | 0 |
2023-06-18 09:10:23.806000
|
https://github.com/yifan-h/CS-GNN
| 77 |
Measuring and improving the use of graph information in graph neural networks
|
https://scholar.google.com/scholar?cluster=6471418699996704565&hl=en&as_sdt=0,10
| 5 | 2,020 |
State-only Imitation with Transition Dynamics Mismatch
| 38 |
iclr
| 3 | 1 |
2023-06-18 09:10:24.010000
|
https://github.com/tgangwani/RL-Indirect-imitation
| 20 |
State-only imitation with transition dynamics mismatch
|
https://scholar.google.com/scholar?cluster=14672237104350314112&hl=en&as_sdt=0,39
| 4 | 2,020 |
Meta Dropout: Learning to Perturb Latent Features for Generalization
| 51 |
iclr
| 4 | 1 |
2023-06-18 09:10:24.213000
|
https://github.com/haebeom-lee/metadrop
| 26 |
Meta dropout: Learning to perturb latent features for generalization
|
https://scholar.google.com/scholar?cluster=14333755794039765777&hl=en&as_sdt=0,11
| 3 | 2,020 |
BlockSwap: Fisher-guided Block Substitution for Network Compression on a Budget
| 25 |
iclr
| 4 | 1 |
2023-06-18 09:10:24.415000
|
https://github.com/BayesWatch/pytorch-blockswap
| 32 |
Blockswap: Fisher-guided block substitution for network compression on a budget
|
https://scholar.google.com/scholar?cluster=2671023600912683387&hl=en&as_sdt=0,10
| 8 | 2,020 |
Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks
| 257 |
iclr
| 18 | 1 |
2023-06-18 09:10:24.618000
|
https://github.com/JHL-HUST/SI-NI-FGSM
| 53 |
Nesterov accelerated gradient and scale invariance for adversarial attacks
|
https://scholar.google.com/scholar?cluster=10642064480465270866&hl=en&as_sdt=0,5
| 4 | 2,020 |
Robustness Verification for Transformers
| 84 |
iclr
| 1 | 0 |
2023-06-18 09:10:24.820000
|
https://github.com/shizhouxing/Robustness-Verification-for-Transformers
| 25 |
Robustness verification for transformers
|
https://scholar.google.com/scholar?cluster=2702221835826609078&hl=en&as_sdt=0,38
| 2 | 2,020 |
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning
| 142 |
iclr
| 9 | 2 |
2023-06-18 09:10:25.024000
|
https://github.com/pokaxpoka/netrand
| 53 |
Network randomization: A simple technique for generalization in deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=6049043144348184316&hl=en&as_sdt=0,5
| 10 | 2,020 |
Tensor Decompositions for Temporal Knowledge Base Completion
| 147 |
iclr
| 19 | 2 |
2023-06-18 09:10:25.227000
|
https://github.com/facebookresearch/tkbc
| 65 |
Tensor decompositions for temporal knowledge base completion
|
https://scholar.google.com/scholar?cluster=18234698389055794905&hl=en&as_sdt=0,10
| 9 | 2,020 |
On Universal Equivariant Set Networks
| 46 |
iclr
| 0 | 1 |
2023-06-18 09:10:25.430000
|
https://github.com/NimrodSegol/On-Universal-Equivariant-Set-Networks
| 10 |
On universal equivariant set networks
|
https://scholar.google.com/scholar?cluster=17434444729278914575&hl=en&as_sdt=0,11
| 1 | 2,020 |
Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$
| 3 |
iclr
| 2 | 0 |
2023-06-18 09:10:25.633000
|
https://github.com/fra31/mmr-universal
| 6 |
Provable robustness against all adversarial -perturbations for
|
https://scholar.google.com/scholar?cluster=14050453960562252546&hl=en&as_sdt=0,33
| 2 | 2,020 |
Don't Use Large Mini-batches, Use Local SGD
| 369 |
iclr
| 6 | 0 |
2023-06-18 09:10:25.836000
|
https://github.com/epfml/LocalSGD-Code
| 39 |
Don't use large mini-batches, use local sgd
|
https://scholar.google.com/scholar?cluster=3406394348267726989&hl=en&as_sdt=0,15
| 10 | 2,020 |
Distributionally Robust Neural Networks
| 852 |
iclr
| 39 | 1 |
2023-06-18 09:10:26.040000
|
https://github.com/kohpangwei/group_DRO
| 184 |
Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
|
https://scholar.google.com/scholar?cluster=11052704904492332793&hl=en&as_sdt=0,14
| 7 | 2,020 |
A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning
| 148 |
iclr
| 15 | 0 |
2023-06-18 09:10:26.243000
|
https://github.com/soochan-lee/CN-DPM
| 91 |
A neural dirichlet process mixture model for task-free continual learning
|
https://scholar.google.com/scholar?cluster=14278617304843676910&hl=en&as_sdt=0,21
| 7 | 2,020 |
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