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Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms | 6 | iclr | 2 | 1 | 2023-06-18 09:44:18.510000 | https://github.com/clio-dl/using-sgnn-for-depression-estimate | 4 | Using graph representation learning with schema encoders to measure the severity of depressive symptoms | https://scholar.google.com/scholar?cluster=18226966908018577857&hl=en&as_sdt=0,33 | 1 | 2,022 |
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning | 441 | iclr | 79 | 2 | 2023-06-18 09:44:18.714000 | https://github.com/facebookresearch/vicreg | 430 | Vicreg: Variance-invariance-covariance regularization for self-supervised learning | https://scholar.google.com/scholar?cluster=14326519942504966909&hl=en&as_sdt=0,11 | 7 | 2,022 |
Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting | 67 | iclr | 7 | 2 | 2023-06-18 09:44:18.917000 | https://github.com/gus-lab/temporal_efficient_training | 34 | Temporal efficient training of spiking neural network via gradient re-weighting | https://scholar.google.com/scholar?cluster=7413408769468810617&hl=en&as_sdt=0,5 | 0 | 2,022 |
Reliable Adversarial Distillation with Unreliable Teachers | 21 | iclr | 2 | 1 | 2023-06-18 09:44:19.120000 | https://github.com/zfancy/iad | 17 | Reliable adversarial distillation with unreliable teachers | https://scholar.google.com/scholar?cluster=14735991802555928714&hl=en&as_sdt=0,48 | 2 | 2,022 |
Delaunay Component Analysis for Evaluation of Data Representations | 7 | iclr | 1 | 1 | 2023-06-18 09:44:19.326000 | https://github.com/petrapoklukar/dca | 10 | Delaunay component analysis for evaluation of data representations | https://scholar.google.com/scholar?cluster=10833565106730763520&hl=en&as_sdt=0,36 | 1 | 2,022 |
Imitation Learning by Reinforcement Learning | 8 | iclr | 0 | 0 | 2023-06-18 09:44:19.529000 | https://github.com/spotify-research/il-by-rl | 1 | Imitation learning by reinforcement learning | https://scholar.google.com/scholar?cluster=5663632794147354936&hl=en&as_sdt=0,5 | 3 | 2,022 |
TAPEX: Table Pre-training via Learning a Neural SQL Executor | 78 | iclr | 32 | 2 | 2023-06-18 09:44:19.732000 | https://github.com/microsoft/Table-Pretraining | 214 | Tapex: Table pre-training via learning a neural sql executor | https://scholar.google.com/scholar?cluster=1887020545839431374&hl=en&as_sdt=0,33 | 4 | 2,022 |
On Robust Prefix-Tuning for Text Classification | 9 | iclr | 2 | 0 | 2023-06-18 09:44:19.936000 | https://github.com/minicheshire/robust-prefix-tuning | 19 | On robust prefix-tuning for text classification | https://scholar.google.com/scholar?cluster=5512236602536653945&hl=en&as_sdt=0,4 | 1 | 2,022 |
Learning Graphon Mean Field Games and Approximate Nash Equilibria | 15 | iclr | 1 | 0 | 2023-06-18 09:44:20.139000 | https://github.com/tudkcui/gmfg-learning | 4 | Learning graphon mean field games and approximate Nash equilibria | https://scholar.google.com/scholar?cluster=18310233350128597723&hl=en&as_sdt=0,5 | 1 | 2,022 |
cosFormer: Rethinking Softmax In Attention | 62 | iclr | 21 | 5 | 2023-06-18 09:44:20.344000 | https://github.com/OpenNLPLab/cosFormer | 148 | cosformer: Rethinking softmax in attention | https://scholar.google.com/scholar?cluster=11701536560712216954&hl=en&as_sdt=0,33 | 5 | 2,022 |
Transferable Adversarial Attack based on Integrated Gradients | 12 | iclr | 4 | 0 | 2023-06-18 09:44:20.557000 | https://github.com/yihuang2016/TAIG | 14 | Transferable adversarial attack based on integrated gradients | https://scholar.google.com/scholar?cluster=12897064558581398673&hl=en&as_sdt=0,5 | 2 | 2,022 |
Topological Graph Neural Networks | 34 | iclr | 11 | 2 | 2023-06-18 09:44:20.761000 | https://github.com/borgwardtlab/togl | 81 | Topological graph neural networks | https://scholar.google.com/scholar?cluster=18101743901347787747&hl=en&as_sdt=0,14 | 8 | 2,022 |
The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Models | 7 | iclr | 0 | 0 | 2023-06-18 09:44:20.963000 | https://github.com/cassidylaidlaw/boltzmann-policy-distribution | 5 | The boltzmann policy distribution: Accounting for systematic suboptimality in human models | https://scholar.google.com/scholar?cluster=403926585745142626&hl=en&as_sdt=0,5 | 1 | 2,022 |
WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection | 20 | iclr | 1 | 4 | 2023-06-18 09:44:21.167000 | https://github.com/spengliang/weakm3d | 19 | Weakm3d: Towards weakly supervised monocular 3d object detection | https://scholar.google.com/scholar?cluster=1602406100270508731&hl=en&as_sdt=0,11 | 2 | 2,022 |
Exploring Memorization in Adversarial Training | 29 | iclr | 1 | 1 | 2023-06-18 09:44:21.371000 | https://github.com/dongyp13/memorization-AT | 18 | Exploring memorization in adversarial training | https://scholar.google.com/scholar?cluster=13986529616809382017&hl=en&as_sdt=0,23 | 1 | 2,022 |
Sound and Complete Neural Network Repair with Minimality and Locality Guarantees | 8 | iclr | 3 | 7 | 2023-06-18 09:44:21.576000 | https://github.com/bu-depend-lab/reassure | 4 | Sound and complete neural network repair with minimality and locality guarantees | https://scholar.google.com/scholar?cluster=862436873685923655&hl=en&as_sdt=0,5 | 1 | 2,022 |
Automated Self-Supervised Learning for Graphs | 31 | iclr | 3 | 0 | 2023-06-18 09:44:21.779000 | https://github.com/ChandlerBang/AutoSSL | 36 | Automated self-supervised learning for graphs | https://scholar.google.com/scholar?cluster=8260281940315648872&hl=en&as_sdt=0,32 | 5 | 2,022 |
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs | 9 | iclr | 2 | 0 | 2023-06-18 09:44:21.982000 | https://github.com/isno0907/localbasis | 7 | Do not escape from the manifold: Discovering the local coordinates on the latent space of GANs | https://scholar.google.com/scholar?cluster=4704378958785987295&hl=en&as_sdt=0,43 | 1 | 2,022 |
GradSign: Model Performance Inference with Theoretical Insights | 4 | iclr | 0 | 0 | 2023-06-18 09:44:22.186000 | https://github.com/cmu-catalyst/gradsign | 5 | Gradsign: Model performance inference with theoretical insights | https://scholar.google.com/scholar?cluster=3694655977867314060&hl=en&as_sdt=0,5 | 1 | 2,022 |
You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks | 30 | iclr | 6 | 1 | 2023-06-18 09:44:22.421000 | https://github.com/jianhao2016/AllSet | 61 | You are allset: A multiset function framework for hypergraph neural networks | https://scholar.google.com/scholar?cluster=2657795859999531247&hl=en&as_sdt=0,5 | 2 | 2,022 |
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods | 26 | iclr | 13 | 8 | 2023-06-18 09:44:22.643000 | https://github.com/amazon-research/gnn-tail-generalization | 43 | Cold brew: Distilling graph node representations with incomplete or missing neighborhoods | https://scholar.google.com/scholar?cluster=6445832848440992452&hl=en&as_sdt=0,5 | 5 | 2,022 |
How to Train Your MAML to Excel in Few-Shot Classification | 17 | iclr | 5 | 4 | 2023-06-18 09:44:22.862000 | https://github.com/han-jia/unicorn-maml | 24 | How to train your MAML to excel in few-shot classification | https://scholar.google.com/scholar?cluster=3274682944038978071&hl=en&as_sdt=0,5 | 1 | 2,022 |
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer | 314 | iclr | 178 | 24 | 2023-06-18 09:44:23.066000 | https://github.com/apple/ml-cvnets | 1,389 | Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer | https://scholar.google.com/scholar?cluster=5434557493125510443&hl=en&as_sdt=0,47 | 34 | 2,022 |
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks | 27 | iclr | 19 | 4 | 2023-06-18 09:44:23.269000 | https://github.com/automl/nasbench301 | 65 | Surrogate NAS benchmarks: Going beyond the limited search spaces of tabular NAS benchmarks | https://scholar.google.com/scholar?cluster=14512036334804223590&hl=en&as_sdt=0,26 | 12 | 2,022 |
Crystal Diffusion Variational Autoencoder for Periodic Material Generation | 57 | iclr | 45 | 27 | 2023-06-18 09:44:23.473000 | https://github.com/txie-93/cdvae | 131 | Crystal diffusion variational autoencoder for periodic material generation | https://scholar.google.com/scholar?cluster=10416305679920850993&hl=en&as_sdt=0,5 | 3 | 2,022 |
Task Affinity with Maximum Bipartite Matching in Few-Shot Learning | 8 | iclr | 0 | 0 | 2023-06-18 09:44:23.676000 | https://github.com/lephuoccat/TAS-few-shot | 3 | Task affinity with maximum bipartite matching in few-shot learning | https://scholar.google.com/scholar?cluster=10877103114487491040&hl=en&as_sdt=0,44 | 2 | 2,022 |
Know Thyself: Transferable Visual Control Policies Through Robot-Awareness | 1 | iclr | 0 | 0 | 2023-06-18 09:44:23.879000 | https://github.com/penn-pal-lab/robot-aware-control | 4 | Know thyself: Transferable visual control policies through robot-awareness | https://scholar.google.com/scholar?cluster=12842278673686640517&hl=en&as_sdt=0,34 | 2 | 2,022 |
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction | 28 | iclr | 97 | 3 | 2023-06-18 09:44:24.082000 | https://github.com/amzn/pecos | 442 | Node feature extraction by self-supervised multi-scale neighborhood prediction | https://scholar.google.com/scholar?cluster=868307857641759607&hl=en&as_sdt=0,5 | 20 | 2,022 |
On the Learning and Learnability of Quasimetrics | 4 | iclr | 1 | 0 | 2023-06-18 09:44:24.285000 | https://github.com/ssnl/poisson_quasimetric_embedding | 28 | On the learning and learnablity of quasimetrics | https://scholar.google.com/scholar?cluster=12412189900513627559&hl=en&as_sdt=0,10 | 1 | 2,022 |
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling | 2 | iclr | 0 | 0 | 2023-06-18 09:44:24.489000 | https://github.com/gisilvs/EmbeddedModelFlows | 3 | Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling | https://scholar.google.com/scholar?cluster=13875622113438069976&hl=en&as_sdt=0,31 | 2 | 2,022 |
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning | 8 | iclr | 2 | 1 | 2023-06-18 09:44:24.694000 | https://github.com/cr-gjx/ria | 9 | A relational intervention approach for unsupervised dynamics generalization in model-based reinforcement learning | https://scholar.google.com/scholar?cluster=16171191146892627821&hl=en&as_sdt=0,33 | 1 | 2,022 |
VOS: Learning What You Don't Know by Virtual Outlier Synthesis | 80 | iclr | 52 | 1 | 2023-06-18 09:44:24.898000 | https://github.com/deeplearning-wisc/vos | 265 | Vos: Learning what you don't know by virtual outlier synthesis | https://scholar.google.com/scholar?cluster=2027738849340009189&hl=en&as_sdt=0,33 | 8 | 2,022 |
Unsupervised Disentanglement with Tensor Product Representations on the Torus | 3 | iclr | 0 | 0 | 2023-06-18 09:44:25.102000 | https://github.com/rotmanmi/unsupervised-disentanglement-torus | 2 | Unsupervised disentanglement with tensor product representations on the torus | https://scholar.google.com/scholar?cluster=12503699134919857893&hl=en&as_sdt=0,5 | 2 | 2,022 |
FlexConv: Continuous Kernel Convolutions With Differentiable Kernel Sizes | 39 | iclr | 7 | 0 | 2023-06-18 09:44:25.304000 | https://github.com/rjbruin/flexconv | 105 | Flexconv: Continuous kernel convolutions with differentiable kernel sizes | https://scholar.google.com/scholar?cluster=1024278192039187692&hl=en&as_sdt=0,5 | 2 | 2,022 |
Zero Pixel Directional Boundary by Vector Transform | 2 | iclr | 0 | 0 | 2023-06-18 09:44:25.508000 | https://github.com/edomel/boundaryvt | 1 | Zero pixel directional boundary by vector transform | https://scholar.google.com/scholar?cluster=4154866420989883552&hl=en&as_sdt=0,5 | 2 | 2,022 |
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion | 41 | iclr | 11 | 8 | 2023-06-18 09:44:25.712000 | https://github.com/zhaoyanglyu/point_diffusion_refinement | 87 | A conditional point diffusion-refinement paradigm for 3d point cloud completion | https://scholar.google.com/scholar?cluster=4241075093947761257&hl=en&as_sdt=0,22 | 4 | 2,022 |
PoNet: Pooling Network for Efficient Token Mixing in Long Sequences | 5 | iclr | 5 | 3 | 2023-06-18 09:44:25.915000 | https://github.com/lxchtan/ponet | 20 | PoNet: Pooling network for efficient token mixing in long sequences | https://scholar.google.com/scholar?cluster=12721480032939252557&hl=en&as_sdt=0,47 | 1 | 2,022 |
Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios | 16 | iclr | 0 | 0 | 2023-06-18 09:44:26.117000 | https://github.com/zhenxianglance/2classbadetection | 6 | Post-training detection of backdoor attacks for two-class and multi-attack scenarios | https://scholar.google.com/scholar?cluster=12429921260786315326&hl=en&as_sdt=0,27 | 1 | 2,022 |
Dynamic Token Normalization improves Vision Transformers | 8 | iclr | 1 | 0 | 2023-06-18 09:44:26.320000 | https://github.com/wqshao126/dtn | 22 | Dynamic token normalization improves vision transformer | https://scholar.google.com/scholar?cluster=8641842420029450046&hl=en&as_sdt=0,5 | 4 | 2,022 |
Symbolic Learning to Optimize: Towards Interpretability and Scalability | 11 | iclr | 1 | 0 | 2023-06-18 09:44:26.524000 | https://github.com/vita-group/symbolic-learning-to-optimize | 9 | Symbolic learning to optimize: Towards interpretability and scalability | https://scholar.google.com/scholar?cluster=9878665703631985766&hl=en&as_sdt=0,5 | 7 | 2,022 |
Pseudo Numerical Methods for Diffusion Models on Manifolds | 133 | iclr | 27 | 2 | 2023-06-18 09:44:26.726000 | https://github.com/luping-liu/PNDM | 261 | Pseudo numerical methods for diffusion models on manifolds | https://scholar.google.com/scholar?cluster=13911281549093893446&hl=en&as_sdt=0,5 | 7 | 2,022 |
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm | 177 | iclr | 22 | 16 | 2023-06-18 09:44:26.930000 | https://github.com/sense-gvt/declip | 515 | Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm | https://scholar.google.com/scholar?cluster=5003089118769672378&hl=en&as_sdt=0,5 | 18 | 2,022 |
Sparsity Winning Twice: Better Robust Generalization from More Efficient Training | 16 | iclr | 2 | 1 | 2023-06-18 09:44:27.133000 | https://github.com/vita-group/sparsity-win-robust-generalization | 33 | Sparsity winning twice: Better robust generaliztion from more efficient training | https://scholar.google.com/scholar?cluster=6953571021872677&hl=en&as_sdt=0,34 | 6 | 2,022 |
Active Hierarchical Exploration with Stable Subgoal Representation Learning | 5 | iclr | 1 | 0 | 2023-06-18 09:44:27.338000 | https://github.com/siyuanlee/hess | 5 | Active hierarchical exploration with stable subgoal representation learning | https://scholar.google.com/scholar?cluster=16962537436246841648&hl=en&as_sdt=0,5 | 1 | 2,022 |
Deep AutoAugment | 15 | iclr | 4 | 0 | 2023-06-18 09:44:27.540000 | https://github.com/msu-mlsys-lab/deepaa | 55 | Deep autoaugment | https://scholar.google.com/scholar?cluster=4048740970183234421&hl=en&as_sdt=0,5 | 2 | 2,022 |
Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice | 30 | iclr | 5 | 1 | 2023-06-18 09:44:27.743000 | https://github.com/vita-group/vit-anti-oversmoothing | 55 | Anti-oversmoothing in deep vision transformers via the fourier domain analysis: From theory to practice | https://scholar.google.com/scholar?cluster=1886992923455463917&hl=en&as_sdt=0,5 | 8 | 2,022 |
Self-ensemble Adversarial Training for Improved Robustness | 24 | iclr | 3 | 0 | 2023-06-18 09:44:27.947000 | https://github.com/whj363636/self-ensemble-adversarial-training | 12 | Self-ensemble adversarial training for improved robustness | https://scholar.google.com/scholar?cluster=5523117763790476247&hl=en&as_sdt=0,50 | 1 | 2,022 |
Do deep networks transfer invariances across classes? | 8 | iclr | 1 | 2 | 2023-06-18 09:44:28.150000 | https://github.com/allanyangzhou/generative-invariance-transfer | 25 | Do Deep Networks Transfer Invariances Across Classes? | https://scholar.google.com/scholar?cluster=8418380015111535138&hl=en&as_sdt=0,5 | 3 | 2,022 |
Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL | 19 | iclr | 1 | 1 | 2023-06-18 09:44:28.353000 | https://github.com/bmazoure/ctrl_public | 6 | Cross-trajectory representation learning for zero-shot generalization in rl | https://scholar.google.com/scholar?cluster=8504220534031883718&hl=en&as_sdt=0,5 | 1 | 2,022 |
RvS: What is Essential for Offline RL via Supervised Learning? | 59 | iclr | 5 | 0 | 2023-06-18 09:44:28.569000 | https://github.com/scottemmons/rvs | 57 | RvS: What is Essential for Offline RL via Supervised Learning? | https://scholar.google.com/scholar?cluster=12909820441441824737&hl=en&as_sdt=0,5 | 5 | 2,022 |
Learning Versatile Neural Architectures by Propagating Network Codes | 8 | iclr | 7 | 0 | 2023-06-18 09:44:28.772000 | https://github.com/dingmyu/NCP | 36 | Learning versatile neural architectures by propagating network codes | https://scholar.google.com/scholar?cluster=1912446105154115158&hl=en&as_sdt=0,33 | 3 | 2,022 |
Generative Models as a Data Source for Multiview Representation Learning | 49 | iclr | 12 | 4 | 2023-06-18 09:44:28.976000 | https://github.com/ali-design/GenRep | 84 | Generative models as a data source for multiview representation learning | https://scholar.google.com/scholar?cluster=13492462163020342656&hl=en&as_sdt=0,47 | 4 | 2,022 |
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training | 12 | iclr | 0 | 0 | 2023-06-18 09:44:29.180000 | https://github.com/tuananhbui89/unified-distributional-robustness | 3 | A unified Wasserstein distributional robustness framework for adversarial training | https://scholar.google.com/scholar?cluster=2935072374086624118&hl=en&as_sdt=0,18 | 2 | 2,022 |
miniF2F: a cross-system benchmark for formal Olympiad-level mathematics | 19 | iclr | 35 | 6 | 2023-06-18 09:44:29.383000 | https://github.com/openai/minif2f | 194 | Minif2f: a cross-system benchmark for formal olympiad-level mathematics | https://scholar.google.com/scholar?cluster=11007110813493819221&hl=en&as_sdt=0,33 | 96 | 2,022 |
Acceleration of Federated Learning with Alleviated Forgetting in Local Training | 23 | iclr | 3 | 0 | 2023-06-18 09:44:29.610000 | https://github.com/zoesgithub/fedreg | 19 | Acceleration of federated learning with alleviated forgetting in local training | https://scholar.google.com/scholar?cluster=637540214191418314&hl=en&as_sdt=0,5 | 2 | 2,022 |
Discovering Invariant Rationales for Graph Neural Networks | 68 | iclr | 14 | 2 | 2023-06-18 09:44:29.814000 | https://github.com/wuyxin/dir-gnn | 84 | Discovering invariant rationales for graph neural networks | https://scholar.google.com/scholar?cluster=6763314222815951542&hl=en&as_sdt=0,23 | 5 | 2,022 |
Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings | 17 | iclr | 1 | 0 | 2023-06-18 09:44:30.018000 | https://github.com/BoChenGroup/WeTe | 3 | Representing mixtures of word embeddings with mixtures of topic embeddings | https://scholar.google.com/scholar?cluster=3518295104208201525&hl=en&as_sdt=0,5 | 0 | 2,022 |
Generative Modeling with Optimal Transport Maps | 32 | iclr | 9 | 0 | 2023-06-18 09:44:30.221000 | https://github.com/LituRout/OptimalTransportModeling | 37 | Generative modeling with optimal transport maps | https://scholar.google.com/scholar?cluster=7494071659521623034&hl=en&as_sdt=0,21 | 2 | 2,022 |
Focus on the Common Good: Group Distributional Robustness Follows | 11 | iclr | 2 | 0 | 2023-06-18 09:44:30.425000 | https://github.com/vihari/cgd | 5 | Focus on the common good: Group distributional robustness follows | https://scholar.google.com/scholar?cluster=7624890232005107632&hl=en&as_sdt=0,5 | 1 | 2,022 |
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification | 28 | iclr | 20 | 2 | 2023-06-18 09:44:30.628000 | https://github.com/Wensi-Tang/OS-CNN | 102 | Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification | https://scholar.google.com/scholar?cluster=2762110290029984845&hl=en&as_sdt=0,47 | 3 | 2,022 |
Decoupled Adaptation for Cross-Domain Object Detection | 17 | iclr | 0 | 3 | 2023-06-18 09:44:30.831000 | https://github.com/thuml/Decoupled-Adaptation-for-Cross-Domain-Object-Detection | 12 | Decoupled adaptation for cross-domain object detection | https://scholar.google.com/scholar?cluster=15741647354170922060&hl=en&as_sdt=0,5 | 4 | 2,022 |
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework | 170 | iclr | 45 | 1 | 2023-06-18 09:44:31.034000 | https://github.com/ma-xu/pointmlp-pytorch | 364 | Rethinking network design and local geometry in point cloud: A simple residual MLP framework | https://scholar.google.com/scholar?cluster=10170039268493179331&hl=en&as_sdt=0,44 | 4 | 2,022 |
MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts | 33 | iclr | 3 | 4 | 2023-06-18 09:44:31.237000 | https://github.com/weixin-liang/metashift | 94 | Metashift: A dataset of datasets for evaluating contextual distribution shifts and training conflicts | https://scholar.google.com/scholar?cluster=11769188169482891384&hl=en&as_sdt=0,5 | 2 | 2,022 |
Efficient and Differentiable Conformal Prediction with General Function Classes | 8 | iclr | 0 | 0 | 2023-06-18 09:44:31.440000 | https://github.com/allenbai01/cp-gen | 3 | Efficient and differentiable conformal prediction with general function classes | https://scholar.google.com/scholar?cluster=54755366591296300&hl=en&as_sdt=0,5 | 1 | 2,022 |
Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps | 2 | iclr | 0 | 0 | 2023-06-18 09:44:31.643000 | https://github.com/nicocourts/bundle-networks | 0 | Bundle networks: Fiber bundles, local trivializations, and a generative approach to exploring many-to-one maps | https://scholar.google.com/scholar?cluster=792839043857596844&hl=en&as_sdt=0,47 | 3 | 2,022 |
On the role of population heterogeneity in emergent communication | 8 | iclr | 0 | 0 | 2023-06-18 09:44:31.846000 | https://github.com/mathieurita/population | 4 | On the role of population heterogeneity in emergent communication | https://scholar.google.com/scholar?cluster=9738620591444184168&hl=en&as_sdt=0,31 | 2 | 2,022 |
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception | 5 | iclr | 4 | 1 | 2023-06-18 09:44:32.050000 | https://github.com/yurongyou/hindsight | 34 | Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception | https://scholar.google.com/scholar?cluster=15674924686724150204&hl=en&as_sdt=0,5 | 6 | 2,022 |
Language-driven Semantic Segmentation | 137 | iclr | 65 | 4 | 2023-06-18 09:44:32.253000 | https://github.com/isl-org/lang-seg | 529 | Language-driven semantic segmentation | https://scholar.google.com/scholar?cluster=17851834070670501779&hl=en&as_sdt=0,1 | 18 | 2,022 |
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative | 12 | iclr | 0 | 1 | 2023-06-18 09:44:32.457000 | https://github.com/ldery/tartan | 8 | Should we be pre-training? an argument for end-task aware training as an alternative | https://scholar.google.com/scholar?cluster=18049548390488755873&hl=en&as_sdt=0,5 | 5 | 2,022 |
Learning Super-Features for Image Retrieval | 15 | iclr | 6 | 4 | 2023-06-18 09:44:32.661000 | https://github.com/naver/fire | 108 | Learning super-features for image retrieval | https://scholar.google.com/scholar?cluster=18354886281666747980&hl=en&as_sdt=0,5 | 9 | 2,022 |
Few-Shot Backdoor Attacks on Visual Object Tracking | 30 | iclr | 1 | 0 | 2023-06-18 09:44:32.864000 | https://github.com/hxzhong1997/fsba | 9 | Few-shot backdoor attacks on visual object tracking | https://scholar.google.com/scholar?cluster=14007756108337436&hl=en&as_sdt=0,39 | 1 | 2,022 |
Backdoor Defense via Decoupling the Training Process | 60 | iclr | 5 | 1 | 2023-06-18 09:44:33.067000 | https://github.com/sclbd/dbd | 21 | Backdoor defense via decoupling the training process | https://scholar.google.com/scholar?cluster=11519386362177505857&hl=en&as_sdt=0,5 | 1 | 2,022 |
Reverse Engineering of Imperceptible Adversarial Image Perturbations | 9 | iclr | 0 | 2 | 2023-06-18 09:44:33.271000 | https://github.com/yifanfanfanfan/reverse-engineering-of-imperceptible-adversarial-image-perturbations | 9 | Reverse engineering of imperceptible adversarial image perturbations | https://scholar.google.com/scholar?cluster=16789227603564642801&hl=en&as_sdt=0,32 | 1 | 2,022 |
DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR | 170 | iclr | 64 | 17 | 2023-06-18 09:44:33.475000 | https://github.com/slongliu/dab-detr | 400 | Dab-detr: Dynamic anchor boxes are better queries for detr | https://scholar.google.com/scholar?cluster=11838073149065061192&hl=en&as_sdt=0,33 | 15 | 2,022 |
Signing the Supermask: Keep, Hide, Invert | 5 | iclr | 2 | 0 | 2023-06-18 09:44:33.678000 | https://github.com/kosnil/signed_supermasks | 2 | Signing the supermask: Keep, hide, invert | https://scholar.google.com/scholar?cluster=10618821989752755915&hl=en&as_sdt=0,33 | 1 | 2,022 |
Bootstrapping Semantic Segmentation with Regional Contrast | 52 | iclr | 24 | 0 | 2023-06-18 09:44:33.882000 | https://github.com/lorenmt/reco | 146 | Bootstrapping semantic segmentation with regional contrast | https://scholar.google.com/scholar?cluster=12918707374441736964&hl=en&as_sdt=0,33 | 6 | 2,022 |
Generative Principal Component Analysis | 8 | iclr | 1 | 0 | 2023-06-18 09:44:34.085000 | https://github.com/liuzq09/GenerativePCA | 3 | Generative principal component analysis | https://scholar.google.com/scholar?cluster=8634676628677545132&hl=en&as_sdt=0,11 | 1 | 2,022 |
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks | 33 | iclr | 21 | 0 | 2023-06-18 09:44:34.289000 | https://github.com/Graph-Machine-Learning-Group/grin | 88 | Filling the g_ap_s: Multivariate time series imputation by graph neural networks | https://scholar.google.com/scholar?cluster=14193757514570115275&hl=en&as_sdt=0,10 | 3 | 2,022 |
Multimeasurement Generative Models | 4 | iclr | 0 | 0 | 2023-06-18 09:44:34.492000 | https://github.com/nnaisense/mems | 3 | Multimeasurement Generative Models | https://scholar.google.com/scholar?cluster=5398070140675307056&hl=en&as_sdt=0,5 | 3 | 2,022 |
Information Gain Propagation: a New Way to Graph Active Learning with Soft Labels | 0 | iclr | 5 | 2 | 2023-06-18 09:44:34.696000 | https://github.com/zwt233/igp | 4 | Information Gain Propagation: a new way to Graph Active Learning with Soft Labels | https://scholar.google.com/scholar?cluster=4290124558616540696&hl=en&as_sdt=0,5 | 1 | 2,022 |
Stein Latent Optimization for Generative Adversarial Networks | 1 | iclr | 0 | 0 | 2023-06-18 09:44:34.900000 | https://github.com/shinyflight/SLOGAN | 4 | Stein latent optimization for generative adversarial networks | https://scholar.google.com/scholar?cluster=14809143039614633477&hl=en&as_sdt=0,5 | 2 | 2,022 |
Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity | 53 | iclr | 13 | 9 | 2023-06-18 09:44:35.103000 | https://github.com/kakaobrain/sparse-detr | 133 | Sparse detr: Efficient end-to-end object detection with learnable sparsity | https://scholar.google.com/scholar?cluster=18202446654995980467&hl=en&as_sdt=0,5 | 12 | 2,022 |
How Low Can We Go: Trading Memory for Error in Low-Precision Training | 3 | iclr | 0 | 0 | 2023-06-18 09:44:35.306000 | https://github.com/barterer/lp | 0 | How low can we go: Trading memory for error in low-precision training | https://scholar.google.com/scholar?cluster=652848499450213393&hl=en&as_sdt=0,5 | 1 | 2,022 |
In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications | 5 | iclr | 0 | 0 | 2023-06-18 09:44:35.509000 | https://github.com/bgleon/latent-goal-architectures | 1 | In a nutshell, the human asked for this: Latent goals for following temporal specifications | https://scholar.google.com/scholar?cluster=14969448845199870512&hl=en&as_sdt=0,33 | 2 | 2,022 |
Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation | 7 | iclr | 0 | 0 | 2023-06-18 09:44:35.714000 | https://github.com/davzha/multiset-equivariance | 11 | Multiset-equivariant set prediction with approximate implicit differentiation | https://scholar.google.com/scholar?cluster=473656227219457535&hl=en&as_sdt=0,5 | 3 | 2,022 |
Modular Lifelong Reinforcement Learning via Neural Composition | 18 | iclr | 3 | 1 | 2023-06-18 09:44:35.917000 | https://github.com/lifelong-ml/mendez2022modularlifelongrl | 12 | Modular lifelong reinforcement learning via neural composition | https://scholar.google.com/scholar?cluster=17042814609795844207&hl=en&as_sdt=0,21 | 2 | 2,022 |
Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks | 43 | iclr | 13 | 4 | 2023-06-18 09:44:36.120000 | https://github.com/putshua/SNN_conversion_QCFS | 30 | Optimal ANN-SNN conversion for high-accuracy and ultra-low-latency spiking neural networks | https://scholar.google.com/scholar?cluster=17393160110870135225&hl=en&as_sdt=0,47 | 1 | 2,022 |
AS-MLP: An Axial Shifted MLP Architecture for Vision | 105 | iclr | 10 | 1 | 2023-06-18 09:44:36.324000 | https://github.com/svip-lab/AS-MLP | 116 | As-mlp: An axial shifted mlp architecture for vision | https://scholar.google.com/scholar?cluster=1534689713476232636&hl=en&as_sdt=0,33 | 5 | 2,022 |
Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference | 12 | iclr | 4 | 0 | 2023-06-18 09:44:36.526000 | https://github.com/naver-ai/i-blurry | 39 | Online continual learning on class incremental blurry task configuration with anytime inference | https://scholar.google.com/scholar?cluster=5710319088637309523&hl=en&as_sdt=0,44 | 1 | 2,022 |
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations | 72 | iclr | 15 | 0 | 2023-06-18 09:44:36.730000 | https://github.com/ucsc-real/cifar-10-100n | 136 | Learning with noisy labels revisited: A study using real-world human annotations | https://scholar.google.com/scholar?cluster=765841518981894990&hl=en&as_sdt=0,43 | 5 | 2,022 |
Learning to Annotate Part Segmentation with Gradient Matching | 7 | iclr | 0 | 0 | 2023-06-18 09:44:36.934000 | https://github.com/yangyu12/lagm | 12 | Learning to annotate part segmentation with gradient matching | https://scholar.google.com/scholar?cluster=16141754978886952440&hl=en&as_sdt=0,5 | 3 | 2,022 |
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation | 98 | iclr | 26 | 2 | 2023-06-18 09:44:37.137000 | https://github.com/ofirpress/attention_with_linear_biases | 324 | Train short, test long: Attention with linear biases enables input length extrapolation | https://scholar.google.com/scholar?cluster=3347460907170213441&hl=en&as_sdt=0,39 | 11 | 2,022 |
Learning Temporally Causal Latent Processes from General Temporal Data | 23 | iclr | 4 | 0 | 2023-06-18 09:44:37.341000 | https://github.com/weirayao/leap | 23 | Learning temporally causal latent processes from general temporal data | https://scholar.google.com/scholar?cluster=14364754714073733596&hl=en&as_sdt=0,14 | 2 | 2,022 |
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning | 19 | iclr | 3 | 0 | 2023-06-18 09:44:37.546000 | https://github.com/ucsc-real/disparate-ssl | 4 | The rich get richer: Disparate impact of semi-supervised learning | https://scholar.google.com/scholar?cluster=7060479972986139346&hl=en&as_sdt=0,5 | 2 | 2,022 |
Bregman Gradient Policy Optimization | 9 | iclr | 1 | 0 | 2023-06-18 09:44:37.757000 | https://github.com/gaosh/bgpo | 3 | Bregman gradient policy optimization | https://scholar.google.com/scholar?cluster=17535380024235547901&hl=en&as_sdt=0,5 | 1 | 2,022 |
Dropout Q-Functions for Doubly Efficient Reinforcement Learning | 24 | iclr | 2 | 0 | 2023-06-18 09:44:37.961000 | https://github.com/TakuyaHiraoka/Dropout-Q-Functions-for-Doubly-Efficient-Reinforcement-Learning | 44 | Dropout q-functions for doubly efficient reinforcement learning | https://scholar.google.com/scholar?cluster=207538077714334096&hl=en&as_sdt=0,31 | 4 | 2,022 |
Uncertainty Modeling for Out-of-Distribution Generalization | 48 | iclr | 14 | 3 | 2023-06-18 09:44:38.165000 | https://github.com/lixiaotong97/dsu | 114 | Uncertainty modeling for out-of-distribution generalization | https://scholar.google.com/scholar?cluster=18401330697518830514&hl=en&as_sdt=0,5 | 3 | 2,022 |
Online Adversarial Attacks | 7 | iclr | 6 | 0 | 2023-06-18 09:44:38.375000 | https://github.com/facebookresearch/OnlineAttacks | 12 | Online adversarial attacks | https://scholar.google.com/scholar?cluster=10843150111517715745&hl=en&as_sdt=0,5 | 6 | 2,022 |
Anytime Dense Prediction with Confidence Adaptivity | 7 | iclr | 0 | 0 | 2023-06-18 09:44:38.588000 | https://github.com/liuzhuang13/anytime | 44 | Anytime dense prediction with confidence adaptivity | https://scholar.google.com/scholar?cluster=14058160425117298434&hl=en&as_sdt=0,5 | 3 | 2,022 |
Unsupervised Discovery of Object Radiance Fields | 60 | iclr | 26 | 2 | 2023-06-18 09:44:38.791000 | https://github.com/KovenYu/uORF | 158 | Unsupervised discovery of object radiance fields | https://scholar.google.com/scholar?cluster=10064360192629959715&hl=en&as_sdt=0,44 | 9 | 2,022 |
Subsets and Splits