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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 |
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