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Dynamic Time Lag Regression: Predicting What & When
| 9
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iclr
| 1
| 7
|
2023-06-18 09:10:06.100000
|
https://github.com/transcendent-ai-labs/PlasmaML
| 16
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Dynamic Time Lag Regression: Predicting What and When
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https://scholar.google.com/scholar?cluster=5170552035479326246&hl=en&as_sdt=0,37
| 7
| 2,020
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Unpaired Point Cloud Completion on Real Scans using Adversarial Training
| 96
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iclr
| 11
| 1
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2023-06-18 09:10:06.302000
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https://github.com/xuelin-chen/pcl2pcl-gan-pub
| 81
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Unpaired point cloud completion on real scans using adversarial training
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https://scholar.google.com/scholar?cluster=6319477762897752803&hl=en&as_sdt=0,5
| 7
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Selection via Proxy: Efficient Data Selection for Deep Learning
| 133
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iclr
| 19
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2023-06-18 09:10:06.506000
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https://github.com/stanford-futuredata/selection-via-proxy
| 78
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Selection via proxy: Efficient data selection for deep learning
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https://scholar.google.com/scholar?cluster=10606664093807319412&hl=en&as_sdt=0,32
| 8
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Global Relational Models of Source Code
| 194
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iclr
| 20
| 3
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2023-06-18 09:10:06.708000
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https://github.com/VHellendoorn/ICLR20-Great
| 79
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Global relational models of source code
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https://scholar.google.com/scholar?cluster=5949441341653621917&hl=en&as_sdt=0,5
| 4
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| 103
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iclr
| 2
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2023-06-18 09:10:06.912000
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https://github.com/ashafahi/RobustTransferLWF
| 16
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Adversarially robust transfer learning
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https://scholar.google.com/scholar?cluster=247907928453605112&hl=en&as_sdt=0,47
| 4
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Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
| 119
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iclr
| 6
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2023-06-18 09:10:07.115000
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https://github.com/IBM/model-sanitization
| 22
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Bridging mode connectivity in loss landscapes and adversarial robustness
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https://scholar.google.com/scholar?cluster=14988732432147772285&hl=en&as_sdt=0,33
| 7
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Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
| 491
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iclr
| 136
| 44
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2023-06-18 09:10:07.317000
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https://github.com/google-research/meta-dataset
| 698
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Meta-dataset: A dataset of datasets for learning to learn from few examples
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https://scholar.google.com/scholar?cluster=14266702502378757393&hl=en&as_sdt=0,32
| 24
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Deep Imitative Models for Flexible Inference, Planning, and Control
| 124
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iclr
| 14
| 19
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2023-06-18 09:10:07.521000
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https://github.com/nrhine1/deep_imitative_models
| 68
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Deep imitative models for flexible inference, planning, and control
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https://scholar.google.com/scholar?cluster=599185864570432210&hl=en&as_sdt=0,45
| 3
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CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
| 75
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iclr
| 10
| 0
|
2023-06-18 09:10:07.723000
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https://github.com/011235813/cm3
| 47
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Cm3: Cooperative multi-goal multi-stage multi-agent reinforcement learning
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https://scholar.google.com/scholar?cluster=11188676090053014781&hl=en&as_sdt=0,3
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Robust And Interpretable Blind Image Denoising Via Bias-Free Convolutional Neural Networks
| 84
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iclr
| 9
| 3
|
2023-06-18 09:10:07.925000
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https://github.com/LabForComputationalVision/bias_free_denoising
| 36
|
Robust and interpretable blind image denoising via bias-free convolutional neural networks
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https://scholar.google.com/scholar?cluster=11707547899272178627&hl=en&as_sdt=0,36
| 5
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DeepV2D: Video to Depth with Differentiable Structure from Motion
| 146
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iclr
| 89
| 28
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2023-06-18 09:10:08.128000
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https://github.com/princeton-vl/DeepV2D
| 598
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Deepv2d: Video to depth with differentiable structure from motion
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https://scholar.google.com/scholar?cluster=564045569449021652&hl=en&as_sdt=0,33
| 20
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Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
| 142
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iclr
| 27
| 10
|
2023-06-18 09:10:08.331000
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https://github.com/cmhcbb/attackbox
| 50
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Sign-opt: A query-efficient hard-label adversarial attack
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https://scholar.google.com/scholar?cluster=4337120578340154737&hl=en&as_sdt=0,5
| 5
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Fast is better than free: Revisiting adversarial training
| 869
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iclr
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| 2
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2023-06-18 09:10:08.534000
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https://github.com/locuslab/fast_adversarial
| 385
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Fast is better than free: Revisiting adversarial training
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https://scholar.google.com/scholar?cluster=227717459026762223&hl=en&as_sdt=0,6
| 12
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DBA: Distributed Backdoor Attacks against Federated Learning
| 377
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iclr
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2023-06-18 09:10:08.737000
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https://github.com/AI-secure/DBA
| 134
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Dba: Distributed backdoor attacks against federated learning
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https://scholar.google.com/scholar?cluster=12314378493827075057&hl=en&as_sdt=0,1
| 2
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DeFINE: Deep Factorized Input Token Embeddings for Neural Sequence Modeling
| 19
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iclr
| 50
| 7
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2023-06-18 09:10:08.941000
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https://github.com/sacmehta/delight
| 443
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Define: Deep factorized input token embeddings for neural sequence modeling
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https://scholar.google.com/scholar?cluster=1535018014104631427&hl=en&as_sdt=0,29
| 14
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| 53
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iclr
| 0
| 0
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2023-06-18 09:10:09.143000
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https://github.com/benlansdell/synthfeedback
| 3
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Learning to solve the credit assignment problem
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https://scholar.google.com/scholar?cluster=1954938718512669715&hl=en&as_sdt=0,37
| 5
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Four Things Everyone Should Know to Improve Batch Normalization
| 48
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iclr
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2023-06-18 09:10:09.347000
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https://github.com/ceciliaresearch/four_things_batch_norm
| 20
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Four things everyone should know to improve batch normalization
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https://scholar.google.com/scholar?cluster=8831824515210942226&hl=en&as_sdt=0,5
| 1
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Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
| 312
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iclr
| 116
| 18
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2023-06-18 09:10:09.551000
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https://github.com/mileyan/Pseudo_Lidar_V2
| 539
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Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving
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https://scholar.google.com/scholar?cluster=10904480408184954283&hl=en&as_sdt=0,10
| 40
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Learning to Learn by Zeroth-Order Oracle
| 14
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iclr
| 5
| 0
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2023-06-18 09:10:09.753000
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https://github.com/RYoungJ/ZO-L2L
| 13
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Learning to learn by zeroth-order oracle
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https://scholar.google.com/scholar?cluster=8954748594282159172&hl=en&as_sdt=0,31
| 2
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DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
| 273
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iclr
| 378
| 170
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2023-06-18 09:10:09.955000
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https://github.com/facebookresearch/habitat-api
| 1,109
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Dd-ppo: Learning near-perfect pointgoal navigators from 2.5 billion frames
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https://scholar.google.com/scholar?cluster=4884965845219755657&hl=en&as_sdt=0,6
| 43
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PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
| 38
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iclr
| 1
| 0
|
2023-06-18 09:10:10.159000
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https://github.com/sangdon/PAC-confidence-set
| 5
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PAC confidence sets for deep neural networks via calibrated prediction
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https://scholar.google.com/scholar?cluster=13464804698510313899&hl=en&as_sdt=0,5
| 2
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Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations
| 19
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iclr
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2023-06-18 09:10:10.362000
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https://github.com/cornell-zhang/dnn-gating
| 69
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Precision gating: Improving neural network efficiency with dynamic dual-precision activations
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https://scholar.google.com/scholar?cluster=5604094105865350488&hl=en&as_sdt=0,39
| 9
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Oblique Decision Trees from Derivatives of ReLU Networks
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iclr
| 7
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2023-06-18 09:10:10.564000
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https://github.com/guanghelee/iclr20-lcn
| 20
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Oblique decision trees from derivatives of relu networks
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https://scholar.google.com/scholar?cluster=15458108821420666095&hl=en&as_sdt=0,31
| 4
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Learn to Explain Efficiently via Neural Logic Inductive Learning
| 58
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iclr
| 17
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2023-06-18 09:10:10.768000
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https://github.com/gblackout/NLIL
| 38
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Learn to explain efficiently via neural logic inductive learning
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https://scholar.google.com/scholar?cluster=4550874980727321525&hl=en&as_sdt=0,15
| 4
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Improved memory in recurrent neural networks with sequential non-normal dynamics
| 12
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iclr
| 2
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2023-06-18 09:10:10.971000
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https://github.com/eminorhan/nonnormal-init
| 3
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Improved memory in recurrent neural networks with sequential non-normal dynamics
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https://scholar.google.com/scholar?cluster=2472327505855554396&hl=en&as_sdt=0,26
| 3
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Neural Module Networks for Reasoning over Text
| 121
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iclr
| 14
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2023-06-18 09:10:11.174000
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https://github.com/nitishgupta/nmn-drop
| 120
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Neural module networks for reasoning over text
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https://scholar.google.com/scholar?cluster=2046532742306416986&hl=en&as_sdt=0,5
| 11
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Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling
| 2
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iclr
| 1
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2023-06-18 09:10:11.377000
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https://github.com/BoChenGroup/VHE-GAN
| 9
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Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling
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https://scholar.google.com/scholar?cluster=6283375856940214417&hl=en&as_sdt=0,5
| 2
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Towards Fast Adaptation of Neural Architectures with Meta Learning
| 70
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iclr
| 7
| 2
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2023-06-18 09:10:11.580000
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https://github.com/dongzelian/T-NAS
| 27
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Towards fast adaptation of neural architectures with meta learning
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https://scholar.google.com/scholar?cluster=2375275580093901945&hl=en&as_sdt=0,5
| 3
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Graph Constrained Reinforcement Learning for Natural Language Action Spaces
| 83
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iclr
| 13
| 1
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2023-06-18 09:10:11.783000
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https://github.com/rajammanabrolu/KG-A2C
| 54
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Graph constrained reinforcement learning for natural language action spaces
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https://scholar.google.com/scholar?cluster=15066208654437399788&hl=en&as_sdt=0,5
| 2
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BERTScore: Evaluating Text Generation with BERT
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iclr
| 186
| 12
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2023-06-18 09:10:11.986000
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https://github.com/Tiiiger/bert_score
| 1,161
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Bertscore: Evaluating text generation with bert
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https://scholar.google.com/scholar?cluster=5304773001741994283&hl=en&as_sdt=0,5
| 22
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Composition-based Multi-Relational Graph Convolutional Networks
| 533
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iclr
| 102
| 13
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2023-06-18 09:10:12.190000
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https://github.com/malllabiisc/CompGCN
| 545
|
Composition-based multi-relational graph convolutional networks
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https://scholar.google.com/scholar?cluster=4927480689371858635&hl=en&as_sdt=0,5
| 17
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Gradient-Based Neural DAG Learning
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iclr
| 19
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2023-06-18 09:10:12.393000
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https://github.com/kurowasan/GraN-DAG
| 78
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Gradient-based neural dag learning
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https://scholar.google.com/scholar?cluster=10487378596908501013&hl=en&as_sdt=0,10
| 6
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The Local Elasticity of Neural Networks
| 29
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iclr
| 2
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2023-06-18 09:10:12.596000
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https://github.com/HornHehhf/LocalElasticity
| 6
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The local elasticity of neural networks
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https://scholar.google.com/scholar?cluster=2497659647078092985&hl=en&as_sdt=0,38
| 3
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Convergence of Gradient Methods on Bilinear Zero-Sum Games
| 33
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iclr
| 1
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2023-06-18 09:10:12.799000
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https://github.com/Gordon-Guojun-Zhang/ICLR-2020
| 1
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Convergence of gradient methods on bilinear zero-sum games
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https://scholar.google.com/scholar?cluster=18092221422699658079&hl=en&as_sdt=0,31
| 2
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| 33
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iclr
| 4
| 0
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2023-06-18 09:10:13.002000
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https://github.com/INK-USC/NExT
| 18
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Learning from explanations with neural execution tree
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https://scholar.google.com/scholar?cluster=7878469874238216625&hl=en&as_sdt=0,5
| 6
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Jelly Bean World: A Testbed for Never-Ending Learning
| 19
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iclr
| 14
| 2
|
2023-06-18 09:10:13.205000
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https://github.com/eaplatanios/jelly-bean-world
| 68
|
Jelly bean world: A testbed for never-ending learning
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https://scholar.google.com/scholar?cluster=13920710483001851413&hl=en&as_sdt=0,5
| 6
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Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality
| 32
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iclr
| 3
| 0
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2023-06-18 09:10:13.408000
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https://github.com/sakhanna/SRU_for_GCI
| 21
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Economy statistical recurrent units for inferring nonlinear granger causality
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https://scholar.google.com/scholar?cluster=9739971127623592335&hl=en&as_sdt=0,14
| 2
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Bayesian Meta Sampling for Fast Uncertainty Adaptation
| 17
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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
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Non-Autoregressive Dialog State Tracking
| 49
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iclr
| 3
| 2
|
2023-06-18 09:10:13.814000
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https://github.com/henryhungle/NADST
| 45
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Non-autoregressive dialog state tracking
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https://scholar.google.com/scholar?cluster=13522465904465807685&hl=en&as_sdt=0,5
| 5
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RNNs Incrementally Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients?
| 40
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iclr
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2023-06-18 09:10:14.018000
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https://github.com/anilkagak2/TARNN
| 6
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Rnns incrementally evolving on an equilibrium manifold: A panacea for vanishing and exploding gradients?
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https://scholar.google.com/scholar?cluster=14548762609337726303&hl=en&as_sdt=0,5
| 3
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| 128
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iclr
| 106
| 15
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2023-06-18 09:10:14.220000
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https://github.com/facebookresearch/open_lth
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The early phase of neural network training
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https://scholar.google.com/scholar?cluster=15707294236176535435&hl=en&as_sdt=0,5
| 57
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| 37
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iclr
| 25
| 0
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2023-06-18 09:10:14.424000
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https://github.com/megvii-model/MABN
| 182
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Towards stabilizing batch statistics in backward propagation of batch normalization
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https://scholar.google.com/scholar?cluster=2467606863922912536&hl=en&as_sdt=0,5
| 8
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Single Episode Policy Transfer in Reinforcement Learning
| 27
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iclr
| 3
| 0
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2023-06-18 09:10:14.627000
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https://github.com/011235813/SEPT
| 16
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Single episode policy transfer in reinforcement learning
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https://scholar.google.com/scholar?cluster=2255040216539653326&hl=en&as_sdt=0,14
| 5
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| 360
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iclr
| 41
| 4
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2023-06-18 09:10:14.830000
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https://github.com/urvashik/knnlm
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Generalization through memorization: Nearest neighbor language models
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https://scholar.google.com/scholar?cluster=17433739628027955410&hl=en&as_sdt=0,5
| 7
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Transformer-XH: Multi-Evidence Reasoning with eXtra Hop Attention
| 98
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iclr
| 15
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2023-06-18 09:10:15.034000
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https://github.com/microsoft/Transformer-XH
| 67
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Transformer-xh: Multi-evidence reasoning with extra hop attention
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https://scholar.google.com/scholar?cluster=1330946954324829338&hl=en&as_sdt=0,5
| 8
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A Closer Look at the Optimization Landscapes of Generative Adversarial Networks
| 56
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iclr
| 12
| 0
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2023-06-18 09:10:15.236000
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https://github.com/facebookresearch/GAN-optimization-landscape
| 31
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A closer look at the optimization landscapes of generative adversarial networks
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https://scholar.google.com/scholar?cluster=8697338348379515621&hl=en&as_sdt=0,3
| 6
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Revisiting Self-Training for Neural Sequence Generation
| 191
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iclr
| 8
| 2
|
2023-06-18 09:10:15.440000
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https://github.com/jxhe/self-training-text-generation
| 45
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Revisiting self-training for neural sequence generation
|
https://scholar.google.com/scholar?cluster=7004703497998979134&hl=en&as_sdt=0,47
| 2
| 2,020
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Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators
| 58
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iclr
| 4
| 0
|
2023-06-18 09:10:15.643000
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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
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LambdaNet: Probabilistic Type Inference using Graph Neural Networks
| 88
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iclr
| 12
| 0
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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
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|
Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping
| 22
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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
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|
Decoupling Representation and Classifier for Long-Tailed Recognition
| 786
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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
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|
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
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|
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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