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|---|---|---|---|---|---|---|---|---|---|---|---|
Statistical inference for individual fairness
| 19
|
iclr
| 0
| 0
|
2023-06-18 09:25:11.648000
|
https://github.com/smaityumich/individual-fairness-testing
| 3
|
Statistical inference for individual fairness
|
https://scholar.google.com/scholar?cluster=6638472826434379762&hl=en&as_sdt=0,5
| 2
| 2,021
|
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
| 83
|
iclr
| 20
| 10
|
2023-06-18 09:25:11.857000
|
https://github.com/alfworld/alfworld
| 99
|
Alfworld: Aligning text and embodied environments for interactive learning
|
https://scholar.google.com/scholar?cluster=11544973336902610716&hl=en&as_sdt=0,33
| 6
| 2,021
|
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification
| 64
|
iclr
| 1
| 1
|
2023-06-18 09:25:12.061000
|
https://github.com/utrerf/robust_transfer_learning
| 11
|
Adversarially-trained deep nets transfer better: Illustration on image classification
|
https://scholar.google.com/scholar?cluster=2203642732634467996&hl=en&as_sdt=0,41
| 6
| 2,021
|
Calibration of Neural Networks using Splines
| 65
|
iclr
| 1
| 1
|
2023-06-18 09:25:12.264000
|
https://github.com/kartikgupta-at-anu/spline-calibration
| 15
|
Calibration of neural networks using splines
|
https://scholar.google.com/scholar?cluster=16036759734574308055&hl=en&as_sdt=0,5
| 2
| 2,021
|
Rethinking Positional Encoding in Language Pre-training
| 152
|
iclr
| 26
| 11
|
2023-06-18 09:25:12.467000
|
https://github.com/guolinke/TUPE
| 238
|
Rethinking positional encoding in language pre-training
|
https://scholar.google.com/scholar?cluster=13553136852407909165&hl=en&as_sdt=0,33
| 6
| 2,021
|
Discovering Non-monotonic Autoregressive Orderings with Variational Inference
| 3
|
iclr
| 3
| 0
|
2023-06-18 09:25:12.671000
|
https://github.com/xuanlinli17/autoregressive_inference
| 10
|
Discovering non-monotonic autoregressive orderings with variational inference
|
https://scholar.google.com/scholar?cluster=14307542819344534269&hl=en&as_sdt=0,5
| 2
| 2,021
|
Differentiable Trust Region Layers for Deep Reinforcement Learning
| 12
|
iclr
| 3
| 0
|
2023-06-18 09:25:12.874000
|
https://github.com/boschresearch/trust-region-layers
| 9
|
Differentiable trust region layers for deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=5230487248575578545&hl=en&as_sdt=0,33
| 4
| 2,021
|
SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization
| 85
|
iclr
| 11
| 3
|
2023-06-18 09:25:13.078000
|
https://github.com/SaliencyMix/SaliencyMix
| 29
|
Saliencymix: A saliency guided data augmentation strategy for better regularization
|
https://scholar.google.com/scholar?cluster=13015633056720744259&hl=en&as_sdt=0,47
| 2
| 2,021
|
Task-Agnostic Morphology Evolution
| 12
|
iclr
| 4
| 0
|
2023-06-18 09:25:13.281000
|
https://github.com/jhejna/morphology-opt
| 18
|
Task-agnostic morphology evolution
|
https://scholar.google.com/scholar?cluster=14695430945522716780&hl=en&as_sdt=0,24
| 2
| 2,021
|
Learning Associative Inference Using Fast Weight Memory
| 30
|
iclr
| 5
| 1
|
2023-06-18 09:25:13.484000
|
https://github.com/ischlag/Fast-Weight-Memory-public
| 22
|
Learning associative inference using fast weight memory
|
https://scholar.google.com/scholar?cluster=16934053175834221248&hl=en&as_sdt=0,33
| 2
| 2,021
|
Boost then Convolve: Gradient Boosting Meets Graph Neural Networks
| 30
|
iclr
| 32
| 4
|
2023-06-18 09:25:13.688000
|
https://github.com/nd7141/bgnn
| 147
|
Boost then convolve: Gradient boosting meets graph neural networks
|
https://scholar.google.com/scholar?cluster=10385206345451191815&hl=en&as_sdt=0,33
| 7
| 2,021
|
Network Pruning That Matters: A Case Study on Retraining Variants
| 33
|
iclr
| 0
| 0
|
2023-06-18 09:25:13.892000
|
https://github.com/lehduong/NPTM
| 18
|
Network pruning that matters: A case study on retraining variants
|
https://scholar.google.com/scholar?cluster=11116406662697084057&hl=en&as_sdt=0,22
| 2
| 2,021
|
Differentiable Segmentation of Sequences
| 1
|
iclr
| 2
| 0
|
2023-06-18 09:25:14.094000
|
https://github.com/diozaka/diffseg
| 4
|
Differentiable Segmentation of Sequences
|
https://scholar.google.com/scholar?cluster=460118456936482519&hl=en&as_sdt=0,5
| 1
| 2,021
|
Learning Deep Features in Instrumental Variable Regression
| 41
|
iclr
| 5
| 0
|
2023-06-18 09:25:14.298000
|
https://github.com/liyuan9988/DeepFeatureIV
| 11
|
Learning deep features in instrumental variable regression
|
https://scholar.google.com/scholar?cluster=17960670738858141531&hl=en&as_sdt=0,31
| 1
| 2,021
|
Graph Information Bottleneck for Subgraph Recognition
| 65
|
iclr
| 4
| 0
|
2023-06-18 09:25:14.501000
|
https://github.com/Samyu0304/graph-information-bottleneck-for-Subgraph-Recognition
| 30
|
Graph information bottleneck for subgraph recognition
|
https://scholar.google.com/scholar?cluster=12146903332537302158&hl=en&as_sdt=0,5
| 2
| 2,021
|
In Search of Lost Domain Generalization
| 623
|
iclr
| 250
| 4
|
2023-06-18 09:25:14.705000
|
https://github.com/facebookresearch/DomainBed
| 1,087
|
In search of lost domain generalization
|
https://scholar.google.com/scholar?cluster=5341652609507299465&hl=en&as_sdt=0,5
| 34
| 2,021
|
CoCon: A Self-Supervised Approach for Controlled Text Generation
| 56
|
iclr
| 22
| 5
|
2023-06-18 09:25:14.908000
|
https://github.com/alvinchangw/COCON_ICLR2021
| 87
|
Cocon: A self-supervised approach for controlled text generation
|
https://scholar.google.com/scholar?cluster=5100156024568984026&hl=en&as_sdt=0,47
| 5
| 2,021
|
CT-Net: Channel Tensorization Network for Video Classification
| 31
|
iclr
| 11
| 0
|
2023-06-18 09:25:15.111000
|
https://github.com/Andy1621/CT-Net
| 34
|
Ct-net: Channel tensorization network for video classification
|
https://scholar.google.com/scholar?cluster=14793670932380609397&hl=en&as_sdt=0,5
| 2
| 2,021
|
Symmetry-Aware Actor-Critic for 3D Molecular Design
| 47
|
iclr
| 22
| 7
|
2023-06-18 09:25:15.315000
|
https://github.com/gncs/molgym
| 94
|
Symmetry-aware actor-critic for 3d molecular design
|
https://scholar.google.com/scholar?cluster=6834309222206333717&hl=en&as_sdt=0,5
| 5
| 2,021
|
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
| 174
|
iclr
| 22
| 9
|
2023-06-18 09:25:15.518000
|
https://github.com/googleinterns/wss
| 149
|
Pseudoseg: Designing pseudo labels for semantic segmentation
|
https://scholar.google.com/scholar?cluster=11801417491488488735&hl=en&as_sdt=0,5
| 10
| 2,021
|
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks
| 9
|
iclr
| 0
| 0
|
2023-06-18 09:25:15.722000
|
https://github.com/sgk98/CRM-Better-Mistakes
| 7
|
No cost likelihood manipulation at test time for making better mistakes in deep networks
|
https://scholar.google.com/scholar?cluster=7455201941557048589&hl=en&as_sdt=0,5
| 6
| 2,021
|
Distance-Based Regularisation of Deep Networks for Fine-Tuning
| 24
|
iclr
| 3
| 1
|
2023-06-18 09:25:15.926000
|
https://github.com/henrygouk/mars-finetuning
| 17
|
Distance-based regularisation of deep networks for fine-tuning
|
https://scholar.google.com/scholar?cluster=16025867309498919322&hl=en&as_sdt=0,14
| 3
| 2,021
|
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
| 134
|
iclr
| 90
| 14
|
2023-06-18 09:25:16.129000
|
https://github.com/odegeasslbc/FastGAN-pytorch
| 501
|
Towards faster and stabilized gan training for high-fidelity few-shot image synthesis
|
https://scholar.google.com/scholar?cluster=1230561477008611475&hl=en&as_sdt=0,5
| 10
| 2,021
|
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network
| 53
|
iclr
| 8
| 2
|
2023-06-18 09:25:16.332000
|
https://github.com/chrundle/biprop
| 38
|
Multi-prize lottery ticket hypothesis: Finding accurate binary neural networks by pruning a randomly weighted network
|
https://scholar.google.com/scholar?cluster=10684547264347032569&hl=en&as_sdt=0,5
| 6
| 2,021
|
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization
| 44
|
iclr
| 6
| 3
|
2023-06-18 09:25:16.536000
|
https://github.com/yanghr/BSQ
| 29
|
BSQ: Exploring bit-level sparsity for mixed-precision neural network quantization
|
https://scholar.google.com/scholar?cluster=11996673667923018710&hl=en&as_sdt=0,44
| 2
| 2,021
|
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly
| 11
|
iclr
| 8
| 1
|
2023-06-18 09:25:16.740000
|
https://github.com/YuchenJin/autolrs
| 38
|
Autolrs: Automatic learning-rate schedule by bayesian optimization on the fly
|
https://scholar.google.com/scholar?cluster=8044903521552367518&hl=en&as_sdt=0,5
| 3
| 2,021
|
BERTology Meets Biology: Interpreting Attention in Protein Language Models
| 181
|
iclr
| 46
| 3
|
2023-06-18 09:25:16.944000
|
https://github.com/salesforce/provis
| 273
|
BERTology meets biology: interpreting attention in protein language models
|
https://scholar.google.com/scholar?cluster=2514418869300543698&hl=en&as_sdt=0,5
| 18
| 2,021
|
Learning Task-General Representations with Generative Neuro-Symbolic Modeling
| 11
|
iclr
| 7
| 1
|
2023-06-18 09:25:17.147000
|
https://github.com/rfeinman/GNS-Modeling
| 22
|
Learning task-general representations with generative neuro-symbolic modeling
|
https://scholar.google.com/scholar?cluster=1335404082385789329&hl=en&as_sdt=0,33
| 5
| 2,021
|
Training independent subnetworks for robust prediction
| 127
|
iclr
| 178
| 119
|
2023-06-18 09:25:17.350000
|
https://github.com/google/uncertainty-baselines
| 1,244
|
Training independent subnetworks for robust prediction
|
https://scholar.google.com/scholar?cluster=9264084238315698016&hl=en&as_sdt=0,43
| 20
| 2,021
|
Meta-Learning of Structured Task Distributions in Humans and Machines
| 7
|
iclr
| 2
| 0
|
2023-06-18 09:25:17.554000
|
https://github.com/sreejank/Compositional_MetaRL
| 6
|
Meta-learning of structured task distributions in humans and machines
|
https://scholar.google.com/scholar?cluster=10148595521419901644&hl=en&as_sdt=0,34
| 2
| 2,021
|
BiPointNet: Binary Neural Network for Point Clouds
| 33
|
iclr
| 12
| 4
|
2023-06-18 09:25:17.769000
|
https://github.com/htqin/BiPointNet
| 66
|
Bipointnet: Binary neural network for point clouds
|
https://scholar.google.com/scholar?cluster=2821902497514525897&hl=en&as_sdt=0,5
| 5
| 2,021
|
Benchmarks for Deep Off-Policy Evaluation
| 47
|
iclr
| 11
| 2
|
2023-06-18 09:25:17.973000
|
https://github.com/google-research/deep_ope
| 78
|
Benchmarks for deep off-policy evaluation
|
https://scholar.google.com/scholar?cluster=4005543467911115320&hl=en&as_sdt=0,19
| 8
| 2,021
|
NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation
| 25
|
iclr
| 11
| 0
|
2023-06-18 09:25:18.177000
|
https://github.com/Angtian/NeMo
| 78
|
Nemo: Neural mesh models of contrastive features for robust 3d pose estimation
|
https://scholar.google.com/scholar?cluster=10173086417139954179&hl=en&as_sdt=0,5
| 6
| 2,021
|
On Graph Neural Networks versus Graph-Augmented MLPs
| 30
|
iclr
| 0
| 0
|
2023-06-18 09:25:18.381000
|
https://github.com/leichen2018/GNN_vs_GAMLP
| 5
|
On graph neural networks versus graph-augmented mlps
|
https://scholar.google.com/scholar?cluster=13883666734002011064&hl=en&as_sdt=0,23
| 2
| 2,021
|
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
| 98
|
iclr
| 4
| 2
|
2023-06-18 09:25:18.584000
|
https://github.com/matsuolab/BREMEN
| 49
|
Deployment-efficient reinforcement learning via model-based offline optimization
|
https://scholar.google.com/scholar?cluster=6135669671400204615&hl=en&as_sdt=0,5
| 14
| 2,021
|
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs
| 81
|
iclr
| 26
| 15
|
2023-06-18 09:25:18.788000
|
https://github.com/DeepGraphLearning/RNNLogic
| 105
|
Rnnlogic: Learning logic rules for reasoning on knowledge graphs
|
https://scholar.google.com/scholar?cluster=15092610783958587096&hl=en&as_sdt=0,33
| 6
| 2,021
|
WaNet - Imperceptible Warping-based Backdoor Attack
| 199
|
iclr
| 16
| 2
|
2023-06-18 09:25:18.992000
|
https://github.com/VinAIResearch/Warping-based_Backdoor_Attack-release
| 73
|
Wanet--imperceptible warping-based backdoor attack
|
https://scholar.google.com/scholar?cluster=704714382831762036&hl=en&as_sdt=0,33
| 5
| 2,021
|
Prototypical Contrastive Learning of Unsupervised Representations
| 594
|
iclr
| 75
| 5
|
2023-06-18 09:25:19.195000
|
https://github.com/salesforce/PCL
| 476
|
Prototypical contrastive learning of unsupervised representations
|
https://scholar.google.com/scholar?cluster=298080063887760247&hl=en&as_sdt=0,31
| 16
| 2,021
|
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
| 61
|
iclr
| 5
| 0
|
2023-06-18 09:25:19.398000
|
https://github.com/benbo/interactive-weak-supervision
| 28
|
Interactive weak supervision: Learning useful heuristics for data labeling
|
https://scholar.google.com/scholar?cluster=15628651718896902730&hl=en&as_sdt=0,31
| 3
| 2,021
|
Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation
| 110
|
iclr
| 4
| 0
|
2023-06-18 09:25:19.602000
|
https://github.com/jungokasai/deep-shallow
| 39
|
Deep encoder, shallow decoder: Reevaluating non-autoregressive machine translation
|
https://scholar.google.com/scholar?cluster=9322073775736159949&hl=en&as_sdt=0,7
| 3
| 2,021
|
PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences
| 67
|
iclr
| 11
| 1
|
2023-06-18 09:25:19.804000
|
https://github.com/hehefan/Point-Spatio-Temporal-Convolution
| 76
|
Pstnet: Point spatio-temporal convolution on point cloud sequences
|
https://scholar.google.com/scholar?cluster=2334272316624788650&hl=en&as_sdt=0,29
| 2
| 2,021
|
Prototypical Representation Learning for Relation Extraction
| 33
|
iclr
| 5
| 6
|
2023-06-18 09:25:20.007000
|
https://github.com/Alibaba-NLP/ProtoRE
| 30
|
Prototypical representation learning for relation extraction
|
https://scholar.google.com/scholar?cluster=12544006759905435219&hl=en&as_sdt=0,5
| 6
| 2,021
|
Layer-adaptive Sparsity for the Magnitude-based Pruning
| 62
|
iclr
| 5
| 2
|
2023-06-18 09:25:20.211000
|
https://github.com/jaeho-lee/layer-adaptive-sparsity
| 40
|
Layer-adaptive sparsity for the magnitude-based pruning
|
https://scholar.google.com/scholar?cluster=16870181998029600993&hl=en&as_sdt=0,33
| 1
| 2,021
|
Refining Deep Generative Models via Discriminator Gradient Flow
| 26
|
iclr
| 5
| 0
|
2023-06-18 09:25:20.414000
|
https://github.com/clear-nus/DGflow
| 15
|
Refining deep generative models via discriminator gradient flow
|
https://scholar.google.com/scholar?cluster=6216370278663020566&hl=en&as_sdt=0,5
| 3
| 2,021
|
Lipschitz Recurrent Neural Networks
| 65
|
iclr
| 6
| 0
|
2023-06-18 09:25:20.618000
|
https://github.com/erichson/LipschitzRNN
| 22
|
Lipschitz recurrent neural networks
|
https://scholar.google.com/scholar?cluster=9494951983450732150&hl=en&as_sdt=0,5
| 4
| 2,021
|
Learning Hyperbolic Representations of Topological Features
| 8
|
iclr
| 0
| 0
|
2023-06-18 09:25:20.835000
|
https://github.com/pkyriakis/permanifold
| 4
|
Learning hyperbolic representations of topological features
|
https://scholar.google.com/scholar?cluster=6250242644104147473&hl=en&as_sdt=0,5
| 3
| 2,021
|
Risk-Averse Offline Reinforcement Learning
| 52
|
iclr
| 3
| 1
|
2023-06-18 09:25:21.039000
|
https://github.com/nuria95/O-RAAC
| 31
|
Risk-averse offline reinforcement learning
|
https://scholar.google.com/scholar?cluster=13690519039445695672&hl=en&as_sdt=0,5
| 2
| 2,021
|
Group Equivariant Stand-Alone Self-Attention For Vision
| 38
|
iclr
| 4
| 2
|
2023-06-18 09:25:21.242000
|
https://github.com/dwromero/g_selfatt
| 25
|
Group equivariant stand-alone self-attention for vision
|
https://scholar.google.com/scholar?cluster=6833601088061308138&hl=en&as_sdt=0,10
| 2
| 2,021
|
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning
| 39
|
iclr
| 5
| 1
|
2023-06-18 09:25:21.446000
|
https://github.com/SamuelHorvath/Compressed_SGD_PyTorch
| 11
|
A better alternative to error feedback for communication-efficient distributed learning
|
https://scholar.google.com/scholar?cluster=3097136742513033323&hl=en&as_sdt=0,5
| 3
| 2,021
|
Capturing Label Characteristics in VAEs
| 24
|
iclr
| 4
| 2
|
2023-06-18 09:25:21.650000
|
https://github.com/thwjoy/ccvae
| 10
|
Capturing label characteristics in VAEs
|
https://scholar.google.com/scholar?cluster=9136485523673709879&hl=en&as_sdt=0,33
| 3
| 2,021
|
InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective
| 71
|
iclr
| 4
| 0
|
2023-06-18 09:25:21.852000
|
https://github.com/AI-secure/InfoBERT
| 75
|
Infobert: Improving robustness of language models from an information theoretic perspective
|
https://scholar.google.com/scholar?cluster=12094007183330442951&hl=en&as_sdt=0,14
| 3
| 2,021
|
DrNAS: Dirichlet Neural Architecture Search
| 83
|
iclr
| 13
| 2
|
2023-06-18 09:25:22.056000
|
https://github.com/xiangning-chen/DrNAS
| 39
|
Drnas: Dirichlet neural architecture search
|
https://scholar.google.com/scholar?cluster=10097373512584874749&hl=en&as_sdt=0,5
| 3
| 2,021
|
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration
| 10
|
iclr
| 4
| 0
|
2023-06-18 09:25:22.260000
|
https://github.com/jaekyeom/drop-bottleneck
| 11
|
Drop-bottleneck: Learning discrete compressed representation for noise-robust exploration
|
https://scholar.google.com/scholar?cluster=4970327572686173895&hl=en&as_sdt=0,5
| 1
| 2,021
|
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning
| 25
|
iclr
| 1
| 1
|
2023-06-18 09:25:22.464000
|
https://github.com/SunbowLiu/SurfaceFusion
| 23
|
Understanding and improving encoder layer fusion in sequence-to-sequence learning
|
https://scholar.google.com/scholar?cluster=14614453829953722728&hl=en&as_sdt=0,11
| 4
| 2,021
|
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling
| 6
|
iclr
| 0
| 0
|
2023-06-18 09:25:22.667000
|
https://github.com/djordjemila/sdn
| 34
|
Spatial dependency networks: Neural layers for improved generative image modeling
|
https://scholar.google.com/scholar?cluster=4211572628480421542&hl=en&as_sdt=0,6
| 4
| 2,021
|
Revisiting Locally Supervised Learning: an Alternative to End-to-end Training
| 45
|
iclr
| 18
| 3
|
2023-06-18 09:25:22.870000
|
https://github.com/blackfeather-wang/InfoPro-Pytorch
| 85
|
Revisiting locally supervised learning: an alternative to end-to-end training
|
https://scholar.google.com/scholar?cluster=9055003625249096504&hl=en&as_sdt=0,14
| 4
| 2,021
|
Gradient Origin Networks
| 11
|
iclr
| 18
| 2
|
2023-06-18 09:25:23.073000
|
https://github.com/cwkx/GON
| 157
|
Gradient origin networks
|
https://scholar.google.com/scholar?cluster=861384408190875414&hl=en&as_sdt=0,5
| 11
| 2,021
|
Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets
| 22
|
iclr
| 8
| 3
|
2023-06-18 09:25:23.276000
|
https://github.com/HayeonLee/MetaD2A
| 53
|
Rapid neural architecture search by learning to generate graphs from datasets
|
https://scholar.google.com/scholar?cluster=7579199201764554515&hl=en&as_sdt=0,5
| 4
| 2,021
|
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization
| 14
|
iclr
| 2
| 0
|
2023-06-18 09:25:23.481000
|
https://github.com/bethgelab/testing_visualizations
| 10
|
Exemplary natural images explain CNN activations better than state-of-the-art feature visualization
|
https://scholar.google.com/scholar?cluster=4262811630228932097&hl=en&as_sdt=0,44
| 10
| 2,021
|
Adversarial score matching and improved sampling for image generation
| 62
|
iclr
| 19
| 0
|
2023-06-18 09:25:23.683000
|
https://github.com/AlexiaJM/AdversarialConsistentScoreMatching
| 116
|
Adversarial score matching and improved sampling for image generation
|
https://scholar.google.com/scholar?cluster=10784754295814543422&hl=en&as_sdt=0,44
| 6
| 2,021
|
Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective
| 97
|
iclr
| 3
| 0
|
2023-06-18 09:25:23.886000
|
https://github.com/balcilar/gnn-spectral-expressive-power
| 39
|
Analyzing the expressive power of graph neural networks in a spectral perspective
|
https://scholar.google.com/scholar?cluster=12539425234528098281&hl=en&as_sdt=0,5
| 1
| 2,021
|
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
| 226
|
iclr
| 26
| 1
|
2023-06-18 09:25:24.089000
|
https://github.com/dem123456789/HeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients
| 89
|
HeteroFL: Computation and communication efficient federated learning for heterogeneous clients
|
https://scholar.google.com/scholar?cluster=2499958009868244362&hl=en&as_sdt=0,22
| 2
| 2,021
|
DINO: A Conditional Energy-Based GAN for Domain Translation
| 4
|
iclr
| 2
| 0
|
2023-06-18 09:25:24.294000
|
https://github.com/DinoMan/DINO
| 16
|
Dino: A conditional energy-based gan for domain translation
|
https://scholar.google.com/scholar?cluster=16181191897980218531&hl=en&as_sdt=0,5
| 4
| 2,021
|
Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning
| 45
|
iclr
| 25
| 4
|
2023-06-18 09:25:24.497000
|
https://github.com/twke18/SPML
| 92
|
Universal weakly supervised segmentation by pixel-to-segment contrastive learning
|
https://scholar.google.com/scholar?cluster=2575509645382870246&hl=en&as_sdt=0,43
| 5
| 2,021
|
C-Learning: Horizon-Aware Cumulative Accessibility Estimation
| 1
|
iclr
| 3
| 0
|
2023-06-18 09:25:24.701000
|
https://github.com/layer6ai-labs/CAE
| 3
|
C-learning: Horizon-aware cumulative accessibility estimation
|
https://scholar.google.com/scholar?cluster=1403006878446325518&hl=en&as_sdt=0,44
| 6
| 2,021
|
Neurally Augmented ALISTA
| 11
|
iclr
| 5
| 0
|
2023-06-18 09:25:24.905000
|
https://github.com/feeds/na-alista
| 13
|
Neurally augmented ALISTA
|
https://scholar.google.com/scholar?cluster=3734900423445750140&hl=en&as_sdt=0,36
| 5
| 2,021
|
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
| 60
|
iclr
| 16
| 1
|
2023-06-18 09:25:25.108000
|
https://github.com/CW-Huang/CP-Flow
| 73
|
Convex potential flows: Universal probability distributions with optimal transport and convex optimization
|
https://scholar.google.com/scholar?cluster=10968638702827610347&hl=en&as_sdt=0,36
| 5
| 2,021
|
Wasserstein Embedding for Graph Learning
| 46
|
iclr
| 2
| 1
|
2023-06-18 09:25:25.312000
|
https://github.com/navid-naderi/WEGL
| 25
|
Wasserstein embedding for graph learning
|
https://scholar.google.com/scholar?cluster=318944885595116091&hl=en&as_sdt=0,5
| 3
| 2,021
|
Grounding Language to Autonomously-Acquired Skills via Goal Generation
| 42
|
iclr
| 3
| 0
|
2023-06-18 09:25:25.517000
|
https://github.com/akakzia/decstr
| 15
|
Grounding language to autonomously-acquired skills via goal generation
|
https://scholar.google.com/scholar?cluster=192435658949853668&hl=en&as_sdt=0,34
| 2
| 2,021
|
Isometric Transformation Invariant and Equivariant Graph Convolutional Networks
| 21
|
iclr
| 6
| 0
|
2023-06-18 09:25:25.721000
|
https://github.com/yellowshippo/isogcn-iclr2021
| 41
|
Isometric transformation invariant and equivariant graph convolutional networks
|
https://scholar.google.com/scholar?cluster=8837825832802039712&hl=en&as_sdt=0,1
| 1
| 2,021
|
R-GAP: Recursive Gradient Attack on Privacy
| 57
|
iclr
| 1
| 0
|
2023-06-18 09:25:25.934000
|
https://github.com/JunyiZhu-AI/R-GAP
| 28
|
R-gap: Recursive gradient attack on privacy
|
https://scholar.google.com/scholar?cluster=15519567665502998239&hl=en&as_sdt=0,5
| 2
| 2,021
|
Multiplicative Filter Networks
| 60
|
iclr
| 7
| 1
|
2023-06-18 09:25:26.138000
|
https://github.com/boschresearch/multiplicative-filter-networks
| 72
|
Multiplicative filter networks
|
https://scholar.google.com/scholar?cluster=2058143723489535198&hl=en&as_sdt=0,47
| 8
| 2,021
|
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks
| 45
|
iclr
| 8
| 2
|
2023-06-18 09:25:26.341000
|
https://github.com/RobertCsordas/modules
| 33
|
Are neural nets modular? inspecting functional modularity through differentiable weight masks
|
https://scholar.google.com/scholar?cluster=5376725240371408845&hl=en&as_sdt=0,5
| 0
| 2,021
|
Modeling the Second Player in Distributionally Robust Optimization
| 17
|
iclr
| 7
| 0
|
2023-06-18 09:25:26.546000
|
https://github.com/pmichel31415/P-DRO
| 18
|
Modeling the second player in distributionally robust optimization
|
https://scholar.google.com/scholar?cluster=16015230267051780457&hl=en&as_sdt=0,33
| 2
| 2,021
|
Private Post-GAN Boosting
| 21
|
iclr
| 4
| 6
|
2023-06-18 09:25:26.750000
|
https://github.com/mneunhoe/post-gan-boosting
| 9
|
Private post-GAN boosting
|
https://scholar.google.com/scholar?cluster=937740189813979153&hl=en&as_sdt=0,33
| 4
| 2,021
|
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
| 126
|
iclr
| 171
| 73
|
2023-06-18 09:25:26.953000
|
https://github.com/NVIDIA/flowtron
| 839
|
Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis
|
https://scholar.google.com/scholar?cluster=1579689582070242490&hl=en&as_sdt=0,40
| 31
| 2,021
|
Learning Structural Edits via Incremental Tree Transformations
| 20
|
iclr
| 3
| 0
|
2023-06-18 09:25:27.157000
|
https://github.com/neulab/incremental_tree_edit
| 40
|
Learning structural edits via incremental tree transformations
|
https://scholar.google.com/scholar?cluster=785545051863300366&hl=en&as_sdt=0,1
| 13
| 2,021
|
Sample-Efficient Automated Deep Reinforcement Learning
| 26
|
iclr
| 6
| 0
|
2023-06-18 09:25:27.361000
|
https://github.com/automl/SEARL
| 32
|
Sample-efficient automated deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=1828733930772382760&hl=en&as_sdt=0,33
| 10
| 2,021
|
Multiscale Score Matching for Out-of-Distribution Detection
| 18
|
iclr
| 0
| 1
|
2023-06-18 09:25:27.565000
|
https://github.com/ahsanMah/msma
| 6
|
Multiscale score matching for out-of-distribution detection
|
https://scholar.google.com/scholar?cluster=3312026787172969565&hl=en&as_sdt=0,5
| 3
| 2,021
|
Linear Last-iterate Convergence in Constrained Saddle-point Optimization
| 66
|
iclr
| 1
| 0
|
2023-06-18 09:25:27.768000
|
https://github.com/bahh723/OGDA-last-iterate
| 1
|
Linear last-iterate convergence in constrained saddle-point optimization
|
https://scholar.google.com/scholar?cluster=11705572357313467666&hl=en&as_sdt=0,5
| 4
| 2,021
|
Learning advanced mathematical computations from examples
| 20
|
iclr
| 12
| 0
|
2023-06-18 09:25:27.972000
|
https://github.com/facebookresearch/MathsFromExamples
| 173
|
Learning advanced mathematical computations from examples
|
https://scholar.google.com/scholar?cluster=8069536277199398832&hl=en&as_sdt=0,5
| 10
| 2,021
|
Generalized Energy Based Models
| 83
|
iclr
| 4
| 1
|
2023-06-18 09:25:28.176000
|
https://github.com/MichaelArbel/GeneralizedEBM
| 46
|
Generalized energy based models
|
https://scholar.google.com/scholar?cluster=8950051300346719301&hl=en&as_sdt=0,5
| 4
| 2,021
|
Beyond Categorical Label Representations for Image Classification
| 2
|
iclr
| 8
| 1
|
2023-06-18 09:25:28.380000
|
https://github.com/BoyuanChen/label_representations
| 24
|
Beyond Categorical Label Representations for Image Classification
|
https://scholar.google.com/scholar?cluster=6100870767960512656&hl=en&as_sdt=0,38
| 3
| 2,021
|
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers
| 53
|
iclr
| 12
| 4
|
2023-06-18 09:25:28.583000
|
https://github.com/salesforce/coco-dst
| 52
|
Coco: Controllable counterfactuals for evaluating dialogue state trackers
|
https://scholar.google.com/scholar?cluster=2147186287214525366&hl=en&as_sdt=0,38
| 5
| 2,021
|
Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models
| 22
|
iclr
| 6
| 0
|
2023-06-18 09:25:28.787000
|
https://github.com/point0bar1/ebm-defense
| 17
|
Stochastic security: Adversarial defense using long-run dynamics of energy-based models
|
https://scholar.google.com/scholar?cluster=1702716547695193492&hl=en&as_sdt=0,5
| 2
| 2,021
|
PDE-Driven Spatiotemporal Disentanglement
| 18
|
iclr
| 3
| 0
|
2023-06-18 09:25:28.992000
|
https://github.com/JeremDona/spatiotemporal_variable_separation
| 25
|
Pde-driven spatiotemporal disentanglement
|
https://scholar.google.com/scholar?cluster=11182191467887081005&hl=en&as_sdt=0,5
| 3
| 2,021
|
Directed Acyclic Graph Neural Networks
| 57
|
iclr
| 20
| 1
|
2023-06-18 09:25:29.196000
|
https://github.com/vthost/DAGNN
| 80
|
Directed acyclic graph neural networks
|
https://scholar.google.com/scholar?cluster=13529849835566425247&hl=en&as_sdt=0,33
| 3
| 2,021
|
QPLEX: Duplex Dueling Multi-Agent Q-Learning
| 248
|
iclr
| 25
| 4
|
2023-06-18 09:25:29.400000
|
https://github.com/wjh720/QPLEX
| 71
|
Qplex: Duplex dueling multi-agent q-learning
|
https://scholar.google.com/scholar?cluster=785256568815923824&hl=en&as_sdt=0,23
| 4
| 2,021
|
Learning Energy-Based Models by Diffusion Recovery Likelihood
| 64
|
iclr
| 13
| 6
|
2023-06-18 09:25:29.604000
|
https://github.com/ruiqigao/recovery_likelihood
| 41
|
Learning energy-based models by diffusion recovery likelihood
|
https://scholar.google.com/scholar?cluster=4399294843209736764&hl=en&as_sdt=0,5
| 4
| 2,021
|
Neural Networks for Learning Counterfactual G-Invariances from Single Environments
| 4
|
iclr
| 0
| 0
|
2023-06-18 09:25:29.809000
|
https://github.com/PurdueMINDS/NN_CGInvariance
| 1
|
Neural networks for learning counterfactual g-invariances from single environments
|
https://scholar.google.com/scholar?cluster=11398104939483895599&hl=en&as_sdt=0,5
| 5
| 2,021
|
On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections
| 71
|
iclr
| 4
| 0
|
2023-06-18 09:25:30.025000
|
https://github.com/brandeis-machine-learning/FairAdj
| 7
|
On dyadic fairness: Exploring and mitigating bias in graph connections
|
https://scholar.google.com/scholar?cluster=9084547275542590284&hl=en&as_sdt=0,5
| 2
| 2,021
|
Faster Binary Embeddings for Preserving Euclidean Distances
| 2
|
iclr
| 0
| 0
|
2023-06-18 09:25:30.229000
|
https://github.com/jayzhang0727/Faster-Binary-Embeddings-for-Preserving-Euclidean-Distances
| 3
|
Faster binary embeddings for preserving euclidean distances
|
https://scholar.google.com/scholar?cluster=16441241350533761738&hl=en&as_sdt=0,5
| 1
| 2,021
|
Learning and Evaluating Representations for Deep One-Class Classification
| 134
|
iclr
| 28
| 3
|
2023-06-18 09:25:30.434000
|
https://github.com/google-research/deep_representation_one_class
| 141
|
Learning and evaluating representations for deep one-class classification
|
https://scholar.google.com/scholar?cluster=6458276904017990971&hl=en&as_sdt=0,14
| 7
| 2,021
|
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning
| 6
|
iclr
| 0
| 0
|
2023-06-18 09:25:30.644000
|
https://github.com/NamyeongK/USA_UFGSM
| 1
|
Repurposing pretrained models for robust out-of-domain few-shot learning
|
https://scholar.google.com/scholar?cluster=14110551403229588194&hl=en&as_sdt=0,5
| 0
| 2,021
|
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
| 300
|
iclr
| 38
| 5
|
2023-06-18 09:25:30.871000
|
https://github.com/nayeemrizve/ups
| 199
|
In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning
|
https://scholar.google.com/scholar?cluster=18358012281479028989&hl=en&as_sdt=0,44
| 2
| 2,021
|
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning
| 13
|
iclr
| 1
| 1
|
2023-06-18 09:25:31.074000
|
https://github.com/necla-ml/cater-h
| 6
|
Hopper: Multi-hop transformer for spatiotemporal reasoning
|
https://scholar.google.com/scholar?cluster=15937741305189053323&hl=en&as_sdt=0,14
| 6
| 2,021
|
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization
| 2
|
iclr
| 0
| 1
|
2023-06-18 09:25:31.279000
|
https://github.com/mederrata/spmf
| 2
|
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization
|
https://scholar.google.com/scholar?cluster=17630346324232626458&hl=en&as_sdt=0,5
| 9
| 2,021
|
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology
| 17
|
iclr
| 5
| 2
|
2023-06-18 09:25:31.483000
|
https://github.com/stanfordmlgroup/disentanglement
| 36
|
Evaluating the disentanglement of deep generative models through manifold topology
|
https://scholar.google.com/scholar?cluster=8056390107725360961&hl=en&as_sdt=0,47
| 5
| 2,021
|
Decoupling Global and Local Representations via Invertible Generative Flows
| 12
|
iclr
| 12
| 3
|
2023-06-18 09:25:31.687000
|
https://github.com/XuezheMax/wolf
| 81
|
Decoupling global and local representations via invertible generative flows
|
https://scholar.google.com/scholar?cluster=17803747103962637793&hl=en&as_sdt=0,34
| 4
| 2,021
|
Pre-training Text-to-Text Transformers for Concept-centric Common Sense
| 45
|
iclr
| 0
| 2
|
2023-06-18 09:25:31.890000
|
https://github.com/INK-USC/CALM
| 26
|
Pre-training text-to-text transformers for concept-centric common sense
|
https://scholar.google.com/scholar?cluster=8101587242954788676&hl=en&as_sdt=0,33
| 5
| 2,021
|
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