title
stringlengths 8
155
| citations_google_scholar
int64 0
28.9k
| conference
stringclasses 5
values | forks
int64 0
46.3k
| issues
int64 0
12.2k
| lastModified
stringlengths 19
26
| repo_url
stringlengths 26
130
| stars
int64 0
75.9k
| title_google_scholar
stringlengths 8
155
| url_google_scholar
stringlengths 75
206
| watchers
int64 0
2.77k
| year
int64 2.02k
2.02k
|
|---|---|---|---|---|---|---|---|---|---|---|---|
Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions
| 14 |
icml
| 2 | 0 |
2023-06-17 04:55:18.353000
|
https://github.com/pilancilab/scnn
| 5 |
Fast convex optimization for two-layer relu networks: Equivalent model classes and cone decompositions
|
https://scholar.google.com/scholar?cluster=7077031077028119954&hl=en&as_sdt=0,21
| 3 | 2,022 |
Invariant Ancestry Search
| 1 |
icml
| 0 | 0 |
2023-06-17 04:55:18.559000
|
https://github.com/phillipmogensen/invariantancestrysearch
| 0 |
Invariant Ancestry Search
|
https://scholar.google.com/scholar?cluster=7085135570627495556&hl=en&as_sdt=0,10
| 1 | 2,022 |
SpeqNets: Sparsity-aware permutation-equivariant graph networks
| 21 |
icml
| 3 | 0 |
2023-06-17 04:55:18.765000
|
https://github.com/chrsmrrs/speqnets
| 9 |
Speqnets: Sparsity-aware permutation-equivariant graph networks
|
https://scholar.google.com/scholar?cluster=18273879943488078405&hl=en&as_sdt=0,1
| 1 | 2,022 |
CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer
| 4 |
icml
| 2 | 1 |
2023-06-17 04:55:18.970000
|
https://github.com/YaoMarkMu/CtrlFormer_robotic
| 26 |
Ctrlformer: Learning transferable state representation for visual control via transformer
|
https://scholar.google.com/scholar?cluster=15994281746681133957&hl=en&as_sdt=0,5
| 2 | 2,022 |
AutoSNN: Towards Energy-Efficient Spiking Neural Networks
| 19 |
icml
| 1 | 0 |
2023-06-17 04:55:19.177000
|
https://github.com/nabk89/autosnn
| 11 |
AutoSNN: towards energy-efficient spiking neural networks
|
https://scholar.google.com/scholar?cluster=4509781886252984486&hl=en&as_sdt=0,44
| 1 | 2,022 |
Overcoming Oscillations in Quantization-Aware Training
| 12 |
icml
| 6 | 4 |
2023-06-17 04:55:19.383000
|
https://github.com/qualcomm-ai-research/oscillations-qat
| 35 |
Overcoming oscillations in quantization-aware training
|
https://scholar.google.com/scholar?cluster=7420900147449297727&hl=en&as_sdt=0,33
| 6 | 2,022 |
Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation
| 3 |
icml
| 1 | 0 |
2023-06-17 04:55:19.589000
|
https://github.com/cs-giung/distill-latentbe
| 2 |
Improving ensemble distillation with weight averaging and diversifying perturbation
|
https://scholar.google.com/scholar?cluster=15634605277253421377&hl=en&as_sdt=0,5
| 1 | 2,022 |
Measuring Representational Robustness of Neural Networks Through Shared Invariances
| 2 |
icml
| 0 | 0 |
2023-06-17 04:55:19.796000
|
https://github.com/nvedant07/stir
| 5 |
Measuring Representational Robustness of Neural Networks Through Shared Invariances
|
https://scholar.google.com/scholar?cluster=11535296107699738994&hl=en&as_sdt=0,5
| 2 | 2,022 |
Multi-Task Learning as a Bargaining Game
| 20 |
icml
| 16 | 0 |
2023-06-17 04:55:20.002000
|
https://github.com/avivnavon/nash-mtl
| 116 |
Multi-task learning as a bargaining game
|
https://scholar.google.com/scholar?cluster=3841743488607196482&hl=en&as_sdt=0,5
| 4 | 2,022 |
Variational Inference for Infinitely Deep Neural Networks
| 2 |
icml
| 0 | 1 |
2023-06-17 04:55:20.208000
|
https://github.com/anazaret/unbounded-depth-neural-networks
| 12 |
Variational Inference for Infinitely Deep Neural Networks
|
https://scholar.google.com/scholar?cluster=15923008707496019552&hl=en&as_sdt=0,5
| 1 | 2,022 |
Stable Conformal Prediction Sets
| 7 |
icml
| 0 | 0 |
2023-06-17 04:55:20.414000
|
https://github.com/EugeneNdiaye/stable_conformal_prediction
| 3 |
Stable conformal prediction sets
|
https://scholar.google.com/scholar?cluster=1322086183676915267&hl=en&as_sdt=0,36
| 2 | 2,022 |
Sublinear-Time Clustering Oracle for Signed Graphs
| 0 |
icml
| 0 | 0 |
2023-06-17 04:55:20.621000
|
https://github.com/stefanresearch/signed-oracle
| 0 |
Sublinear-Time Clustering Oracle for Signed Graphs
|
https://scholar.google.com/scholar?cluster=11680644385251401321&hl=en&as_sdt=0,5
| 1 | 2,022 |
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling
| 21 |
icml
| 6 | 0 |
2023-06-17 04:55:20.827000
|
https://github.com/tung-nd/tnp-pytorch
| 42 |
Transformer neural processes: Uncertainty-aware meta learning via sequence modeling
|
https://scholar.google.com/scholar?cluster=8314226561470238527&hl=en&as_sdt=0,39
| 2 | 2,022 |
Improving Transformers with Probabilistic Attention Keys
| 9 |
icml
| 6 | 1 |
2023-06-17 04:55:21.033000
|
https://github.com/minhtannguyen/transformer-mgk
| 20 |
Improving transformers with probabilistic attention keys
|
https://scholar.google.com/scholar?cluster=15369073464631209004&hl=en&as_sdt=0,33
| 1 | 2,022 |
Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs
| 16 |
icml
| 29 | 1 |
2023-06-17 04:55:21.239000
|
https://github.com/twni2016/pomdp-baselines
| 212 |
Recurrent model-free rl can be a strong baseline for many pomdps
|
https://scholar.google.com/scholar?cluster=10952850493674011457&hl=en&as_sdt=0,39
| 5 | 2,022 |
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
| 742 |
icml
| 457 | 23 |
2023-06-17 04:55:21.445000
|
https://github.com/openai/glide-text2im
| 3,226 |
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
|
https://scholar.google.com/scholar?cluster=15472303808406531445&hl=en&as_sdt=0,34
| 142 | 2,022 |
Diffusion Models for Adversarial Purification
| 72 |
icml
| 22 | 0 |
2023-06-17 04:55:21.653000
|
https://github.com/NVlabs/DiffPure
| 163 |
Diffusion models for adversarial purification
|
https://scholar.google.com/scholar?cluster=9166244005732160404&hl=en&as_sdt=0,5
| 5 | 2,022 |
The Primacy Bias in Deep Reinforcement Learning
| 23 |
icml
| 6 | 0 |
2023-06-17 04:55:21.859000
|
https://github.com/evgenii-nikishin/rl_with_resets
| 82 |
The primacy bias in deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=11620338198970862085&hl=en&as_sdt=0,48
| 3 | 2,022 |
Efficient Test-Time Model Adaptation without Forgetting
| 40 |
icml
| 5 | 0 |
2023-06-17 04:55:22.065000
|
https://github.com/mr-eggplant/eata
| 65 |
Efficient test-time model adaptation without forgetting
|
https://scholar.google.com/scholar?cluster=17499416478096807711&hl=en&as_sdt=0,5
| 2 | 2,022 |
Utilizing Expert Features for Contrastive Learning of Time-Series Representations
| 5 |
icml
| 2 | 2 |
2023-06-17 04:55:22.270000
|
https://github.com/boschresearch/expclr
| 14 |
Utilizing expert features for contrastive learning of time-series representations
|
https://scholar.google.com/scholar?cluster=16790455232498977165&hl=en&as_sdt=0,33
| 6 | 2,022 |
Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval
| 33 |
icml
| 21 | 4 |
2023-06-17 04:55:22.477000
|
https://github.com/oatml-markslab/tranception
| 88 |
Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval
|
https://scholar.google.com/scholar?cluster=13139855140556717827&hl=en&as_sdt=0,44
| 5 | 2,022 |
Scalable Deep Gaussian Markov Random Fields for General Graphs
| 2 |
icml
| 3 | 0 |
2023-06-17 04:55:22.684000
|
https://github.com/joeloskarsson/graph-dgmrf
| 4 |
Scalable Deep Gaussian Markov Random Fields for General Graphs
|
https://scholar.google.com/scholar?cluster=16619238478793238405&hl=en&as_sdt=0,48
| 3 | 2,022 |
Zero-shot AutoML with Pretrained Models
| 2 |
icml
| 2 | 0 |
2023-06-17 04:55:22.890000
|
https://github.com/automl/zero-shot-automl-with-pretrained-models
| 35 |
Zero-Shot AutoML with Pretrained Models
|
https://scholar.google.com/scholar?cluster=4155086096102443249&hl=en&as_sdt=0,21
| 9 | 2,022 |
History Compression via Language Models in Reinforcement Learning
| 8 |
icml
| 4 | 0 |
2023-06-17 04:55:23.096000
|
https://github.com/ml-jku/helm
| 38 |
History compression via language models in reinforcement learning
|
https://scholar.google.com/scholar?cluster=3335833011258515063&hl=en&as_sdt=0,19
| 6 | 2,022 |
A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks
| 2 |
icml
| 9 | 2 |
2023-06-17 04:55:23.302000
|
https://github.com/tnbar/tednet
| 64 |
A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks
|
https://scholar.google.com/scholar?cluster=2601266852558996821&hl=en&as_sdt=0,22
| 3 | 2,022 |
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
| 31 |
icml
| 7 | 0 |
2023-06-17 04:55:23.509000
|
https://github.com/p2333/score
| 58 |
Robustness and accuracy could be reconcilable by (proper) definition
|
https://scholar.google.com/scholar?cluster=12573058517676493723&hl=en&as_sdt=0,5
| 2 | 2,022 |
Learning Symmetric Embeddings for Equivariant World Models
| 16 |
icml
| 0 | 1 |
2023-06-17 04:55:23.717000
|
https://github.com/jypark0/sen
| 4 |
Learning symmetric embeddings for equivariant world models
|
https://scholar.google.com/scholar?cluster=17517971134760315540&hl=en&as_sdt=0,33
| 1 | 2,022 |
Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness
| 7 |
icml
| 7 | 0 |
2023-06-17 04:55:23.924000
|
https://github.com/xxxnell/spatial-smoothing
| 70 |
Blurs behave like ensembles: Spatial smoothings to improve accuracy, uncertainty, and robustness
|
https://scholar.google.com/scholar?cluster=11971703868153296298&hl=en&as_sdt=0,33
| 2 | 2,022 |
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
| 24 |
icml
| 3 | 1 |
2023-06-17 04:55:24.130000
|
https://github.com/ml-jku/align-rudder
| 18 |
Align-rudder: Learning from few demonstrations by reward redistribution
|
https://scholar.google.com/scholar?cluster=17099796649634976721&hl=en&as_sdt=0,36
| 6 | 2,022 |
POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging
| 8 |
icml
| 11 | 7 |
2023-06-17 04:55:24.336000
|
https://github.com/shishirpatil/poet
| 127 |
POET: Training neural networks on tiny devices with integrated rematerialization and paging
|
https://scholar.google.com/scholar?cluster=5184430437455623817&hl=en&as_sdt=0,6
| 9 | 2,022 |
Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding
| 32 |
icml
| 1,936 | 479 |
2023-06-17 04:55:24.542000
|
https://github.com/espnet/espnet
| 6,692 |
Branchformer: Parallel mlp-attention architectures to capture local and global context for speech recognition and understanding
|
https://scholar.google.com/scholar?cluster=8709670323739096599&hl=en&as_sdt=0,33
| 179 | 2,022 |
Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
| 30 |
icml
| 46 | 9 |
2023-06-17 04:55:24.751000
|
https://github.com/pengxingang/pocket2mol
| 155 |
Pocket2mol: Efficient molecular sampling based on 3d protein pockets
|
https://scholar.google.com/scholar?cluster=5422392293509643070&hl=en&as_sdt=0,33
| 8 | 2,022 |
Differentiable Top-k Classification Learning
| 8 |
icml
| 0 | 1 |
2023-06-17 04:55:24.977000
|
https://github.com/felix-petersen/difftopk
| 48 |
Differentiable top-k classification learning
|
https://scholar.google.com/scholar?cluster=2888939572667326983&hl=en&as_sdt=0,33
| 3 | 2,022 |
Multi-scale Feature Learning Dynamics: Insights for Double Descent
| 8 |
icml
| 2 | 0 |
2023-06-17 04:55:25.183000
|
https://github.com/nndoubledescent/doubledescent
| 0 |
Multi-scale feature learning dynamics: Insights for double descent
|
https://scholar.google.com/scholar?cluster=15892651020867127021&hl=en&as_sdt=0,33
| 1 | 2,022 |
A Differential Entropy Estimator for Training Neural Networks
| 13 |
icml
| 4 | 0 |
2023-06-17 04:55:25.390000
|
https://github.com/g-pichler/knife
| 9 |
A differential entropy estimator for training neural networks
|
https://scholar.google.com/scholar?cluster=5856117255578319314&hl=en&as_sdt=0,33
| 1 | 2,022 |
Federated Learning with Partial Model Personalization
| 30 |
icml
| 0 | 0 |
2023-06-17 04:55:25.596000
|
https://github.com/krishnap25/fl_partial_personalization
| 1 |
Federated learning with partial model personalization
|
https://scholar.google.com/scholar?cluster=4750968691898857474&hl=en&as_sdt=0,11
| 3 | 2,022 |
Geometric Multimodal Contrastive Representation Learning
| 7 |
icml
| 4 | 0 |
2023-06-17 04:55:25.801000
|
https://github.com/miguelsvasco/gmc
| 17 |
Geometric Multimodal Contrastive Representation Learning
|
https://scholar.google.com/scholar?cluster=1723737180667149201&hl=en&as_sdt=0,50
| 2 | 2,022 |
On the Practicality of Deterministic Epistemic Uncertainty
| 15 |
icml
| 178 | 119 |
2023-06-17 04:55:26.007000
|
https://github.com/google/uncertainty-baselines
| 1,244 |
On the practicality of deterministic epistemic uncertainty
|
https://scholar.google.com/scholar?cluster=10237983835645354047&hl=en&as_sdt=0,33
| 20 | 2,022 |
ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers
| 22 |
icml
| 19 | 4 |
2023-06-17 04:55:26.214000
|
https://github.com/auspicious3000/contentvec
| 277 |
Contentvec: An improved self-supervised speech representation by disentangling speakers
|
https://scholar.google.com/scholar?cluster=16442143470536354603&hl=en&as_sdt=0,26
| 7 | 2,022 |
Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder
| 3 |
icml
| 4 | 0 |
2023-06-17 04:55:26.440000
|
https://github.com/wonderseven/lssae
| 19 |
Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder
|
https://scholar.google.com/scholar?cluster=8021731201291301386&hl=en&as_sdt=0,22
| 3 | 2,022 |
Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence
| 3 |
icml
| 1 | 0 |
2023-06-17 04:55:26.646000
|
https://github.com/zhqiu/ndcg-optimization
| 2 |
Large-scale stochastic optimization of ndcg surrogates for deep learning with provable convergence
|
https://scholar.google.com/scholar?cluster=9377138316635213561&hl=en&as_sdt=0,33
| 1 | 2,022 |
Latent Outlier Exposure for Anomaly Detection with Contaminated Data
| 15 |
icml
| 8 | 1 |
2023-06-17 04:55:26.853000
|
https://github.com/boschresearch/LatentOE-AD
| 34 |
Latent outlier exposure for anomaly detection with contaminated data
|
https://scholar.google.com/scholar?cluster=3679566789459312121&hl=en&as_sdt=0,33
| 4 | 2,022 |
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning
| 7 |
icml
| 0 | 0 |
2023-06-17 04:55:27.059000
|
https://github.com/baichenjia/contrastive-ucb
| 9 |
Contrastive ucb: Provably efficient contrastive self-supervised learning in online reinforcement learning
|
https://scholar.google.com/scholar?cluster=4487688180752876620&hl=en&as_sdt=0,5
| 2 | 2,022 |
Particle Transformer for Jet Tagging
| 10 |
icml
| 25 | 1 |
2023-06-17 04:55:27.265000
|
https://github.com/jet-universe/particle_transformer
| 43 |
Particle transformer for jet tagging
|
https://scholar.google.com/scholar?cluster=12329206017907212560&hl=en&as_sdt=0,23
| 3 | 2,022 |
Winning the Lottery Ahead of Time: Efficient Early Network Pruning
| 5 |
icml
| 2 | 0 |
2023-06-17 04:55:27.480000
|
https://github.com/johnrachwan123/Early-Cropression-via-Gradient-Flow-Preservation
| 15 |
Winning the lottery ahead of time: Efficient early network pruning
|
https://scholar.google.com/scholar?cluster=3167787605705434615&hl=en&as_sdt=0,41
| 2 | 2,022 |
DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale
| 59 |
icml
| 3,110 | 886 |
2023-06-17 04:55:27.718000
|
https://github.com/microsoft/DeepSpeed
| 25,974 |
Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale
|
https://scholar.google.com/scholar?cluster=6450094276419504510&hl=en&as_sdt=0,22
| 290 | 2,022 |
A Closer Look at Smoothness in Domain Adversarial Training
| 20 |
icml
| 4 | 2 |
2023-06-17 04:55:27.927000
|
https://github.com/val-iisc/sdat
| 40 |
A closer look at smoothness in domain adversarial training
|
https://scholar.google.com/scholar?cluster=11164597139581450427&hl=en&as_sdt=0,33
| 14 | 2,022 |
Linear Adversarial Concept Erasure
| 25 |
icml
| 3 | 2 |
2023-06-17 04:55:28.133000
|
https://github.com/shauli-ravfogel/rlace-icml
| 23 |
Linear adversarial concept erasure
|
https://scholar.google.com/scholar?cluster=157683061025883774&hl=en&as_sdt=0,31
| 1 | 2,022 |
Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks
| 15 |
icml
| 0 | 0 |
2023-06-17 04:55:28.339000
|
https://github.com/asafmaman101/imp_reg_htf
| 4 |
Implicit regularization in hierarchical tensor factorization and deep convolutional neural networks
|
https://scholar.google.com/scholar?cluster=12909622448171060632&hl=en&as_sdt=0,33
| 2 | 2,022 |
The dynamics of representation learning in shallow, non-linear autoencoders
| 2 |
icml
| 0 | 0 |
2023-06-17 04:55:28.548000
|
https://github.com/mariaref/nonlinearshallowae
| 5 |
The dynamics of representation learning in shallow, non-linear autoencoders
|
https://scholar.google.com/scholar?cluster=14118431460184328977&hl=en&as_sdt=0,31
| 2 | 2,022 |
Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs
| 1 |
icml
| 0 | 0 |
2023-06-17 04:55:28.755000
|
https://github.com/sjtu-xai-lab/transformation-complexity
| 1 |
Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs
|
https://scholar.google.com/scholar?cluster=1146425504680188001&hl=en&as_sdt=0,33
| 1 | 2,022 |
Benchmarking and Analyzing Point Cloud Classification under Corruptions
| 26 |
icml
| 3 | 0 |
2023-06-17 04:55:28.962000
|
https://github.com/jiawei-ren/modelnetc
| 50 |
Benchmarking and analyzing point cloud classification under corruptions
|
https://scholar.google.com/scholar?cluster=4434116773940428233&hl=en&as_sdt=0,33
| 6 | 2,022 |
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning
| 2 |
icml
| 1 | 0 |
2023-06-17 04:55:29.168000
|
https://github.com/juliusberner/robust_kolmogorov
| 2 |
Robust SDE-based variational formulations for solving linear PDEs via deep learning
|
https://scholar.google.com/scholar?cluster=5839668907631655505&hl=en&as_sdt=0,16
| 1 | 2,022 |
LyaNet: A Lyapunov Framework for Training Neural ODEs
| 17 |
icml
| 3 | 0 |
2023-06-17 04:55:29.382000
|
https://github.com/ivandariojr/lyapunovlearning
| 27 |
LyaNet: A Lyapunov framework for training neural ODEs
|
https://scholar.google.com/scholar?cluster=11176249487221195122&hl=en&as_sdt=0,33
| 3 | 2,022 |
Short-Term Plasticity Neurons Learning to Learn and Forget
| 8 |
icml
| 1 | 0 |
2023-06-17 04:55:29.589000
|
https://github.com/neuromorphiccomputing/stpn
| 17 |
Short-term plasticity neurons learning to learn and forget
|
https://scholar.google.com/scholar?cluster=13353176637859953693&hl=en&as_sdt=0,5
| 4 | 2,022 |
Function-space Inference with Sparse Implicit Processes
| 2 |
icml
| 2 | 0 |
2023-06-17 04:55:29.800000
|
https://github.com/simonrsantana/sparse-implicit-processes
| 1 |
Function-space Inference with Sparse Implicit Processes
|
https://scholar.google.com/scholar?cluster=3087914783084308149&hl=en&as_sdt=0,50
| 1 | 2,022 |
Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images
| 3 |
icml
| 3 | 0 |
2023-06-17 04:55:30.007000
|
https://github.com/tomron27/dd_med
| 2 |
Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images
|
https://scholar.google.com/scholar?cluster=10465544337215443782&hl=en&as_sdt=0,14
| 1 | 2,022 |
A Consistent and Efficient Evaluation Strategy for Attribution Methods
| 18 |
icml
| 4 | 3 |
2023-06-17 04:55:30.216000
|
https://github.com/tleemann/road_evaluation
| 12 |
A consistent and efficient evaluation strategy for attribution methods
|
https://scholar.google.com/scholar?cluster=16933534039020294474&hl=en&as_sdt=0,44
| 1 | 2,022 |
Direct Behavior Specification via Constrained Reinforcement Learning
| 14 |
icml
| 2 | 0 |
2023-06-17 04:55:30.424000
|
https://github.com/ubisoft/directbehaviorspecification
| 8 |
Direct behavior specification via constrained reinforcement learning
|
https://scholar.google.com/scholar?cluster=12930072295285422644&hl=en&as_sdt=0,18
| 2 | 2,022 |
Graph-Coupled Oscillator Networks
| 26 |
icml
| 7 | 1 |
2023-06-17 04:55:30.631000
|
https://github.com/tk-rusch/graphcon
| 39 |
Graph-coupled oscillator networks
|
https://scholar.google.com/scholar?cluster=9009434155878040135&hl=en&as_sdt=0,5
| 3 | 2,022 |
Hindering Adversarial Attacks with Implicit Neural Representations
| 1 |
icml
| 0 | 0 |
2023-06-17 04:55:30.837000
|
https://github.com/deepmind/linac
| 8 |
Hindering Adversarial Attacks with Implicit Neural Representations
|
https://scholar.google.com/scholar?cluster=14287948960663739347&hl=en&as_sdt=0,33
| 2 | 2,022 |
Exploiting Independent Instruments: Identification and Distribution Generalization
| 5 |
icml
| 0 | 0 |
2023-06-17 04:55:31.043000
|
https://github.com/sorawitj/hsic-x
| 5 |
Exploiting independent instruments: Identification and distribution generalization
|
https://scholar.google.com/scholar?cluster=7573181679595557794&hl=en&as_sdt=0,31
| 1 | 2,022 |
LSB: Local Self-Balancing MCMC in Discrete Spaces
| 5 |
icml
| 0 | 0 |
2023-06-17 04:55:31.250000
|
https://github.com/emsansone/lsb
| 2 |
Lsb: Local self-balancing mcmc in discrete spaces
|
https://scholar.google.com/scholar?cluster=4624892797012274460&hl=en&as_sdt=0,11
| 2 | 2,022 |
PoF: Post-Training of Feature Extractor for Improving Generalization
| 1 |
icml
| 0 | 0 |
2023-06-17 04:55:31.463000
|
https://github.com/densoitlab/pof-v1
| 3 |
PoF: Post-Training of Feature Extractor for Improving Generalization
|
https://scholar.google.com/scholar?cluster=1799078834754218861&hl=en&as_sdt=0,31
| 2 | 2,022 |
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings
| 2 |
icml
| 1 | 0 |
2023-06-17 04:55:31.670000
|
https://github.com/meyerscetbon/lp-ci-test
| 0 |
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings
|
https://scholar.google.com/scholar?cluster=14026015450757796884&hl=en&as_sdt=0,33
| 3 | 2,022 |
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs
| 19 |
icml
| 0 | 0 |
2023-06-17 04:55:31.877000
|
https://github.com/meyerscetbon/lineargromov
| 1 |
Linear-time gromov wasserstein distances using low rank couplings and costs
|
https://scholar.google.com/scholar?cluster=883418138428344777&hl=en&as_sdt=0,14
| 2 | 2,022 |
Modeling Irregular Time Series with Continuous Recurrent Units
| 15 |
icml
| 9 | 6 |
2023-06-17 04:55:32.085000
|
https://github.com/boschresearch/continuous-recurrent-units
| 32 |
Modeling irregular time series with continuous recurrent units
|
https://scholar.google.com/scholar?cluster=7564792311041526490&hl=en&as_sdt=0,19
| 7 | 2,022 |
Data-SUITE: Data-centric identification of in-distribution incongruous examples
| 3 |
icml
| 4 | 0 |
2023-06-17 04:55:32.291000
|
https://github.com/seedatnabeel/data-suite
| 7 |
Data-SUITE: Data-centric identification of in-distribution incongruous examples
|
https://scholar.google.com/scholar?cluster=11485689307897239676&hl=en&as_sdt=0,33
| 2 | 2,022 |
Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization
| 5 |
icml
| 0 | 0 |
2023-06-17 04:55:32.497000
|
https://github.com/mselezniova/ntk_beyond_limit
| 0 |
Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization
|
https://scholar.google.com/scholar?cluster=16495366436833298314&hl=en&as_sdt=0,3
| 1 | 2,022 |
Reinforcement Learning with Action-Free Pre-Training from Videos
| 34 |
icml
| 5 | 0 |
2023-06-17 04:55:32.703000
|
https://github.com/younggyoseo/apv
| 46 |
Reinforcement learning with action-free pre-training from videos
|
https://scholar.google.com/scholar?cluster=6676654951334590185&hl=en&as_sdt=0,5
| 4 | 2,022 |
Selective Regression under Fairness Criteria
| 3 |
icml
| 0 | 0 |
2023-06-17 04:55:32.909000
|
https://github.com/abhin02/fair-selective-regression
| 4 |
Selective regression under fairness criteria
|
https://scholar.google.com/scholar?cluster=11829060385063117064&hl=en&as_sdt=0,33
| 1 | 2,022 |
A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning
| 8 |
icml
| 1 | 0 |
2023-06-17 04:55:33.115000
|
https://github.com/architsharma97/medal
| 4 |
A state-distribution matching approach to non-episodic reinforcement learning
|
https://scholar.google.com/scholar?cluster=14448955307324292158&hl=en&as_sdt=0,31
| 2 | 2,022 |
Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold
| 4 |
icml
| 0 | 0 |
2023-06-17 04:55:33.322000
|
https://github.com/fietelab/mesh
| 1 |
Content addressable memory without catastrophic forgetting by heteroassociation with a fixed scaffold
|
https://scholar.google.com/scholar?cluster=16874084475877050820&hl=en&as_sdt=0,5
| 4 | 2,022 |
DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning
| 2 |
icml
| 0 | 0 |
2023-06-17 04:55:33.528000
|
https://github.com/IntelLabs/DNS
| 1 |
DNS: Determinantal point process based neural network sampler for ensemble reinforcement learning
|
https://scholar.google.com/scholar?cluster=16987143666282140914&hl=en&as_sdt=0,34
| 2 | 2,022 |
PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs
| 3 |
icml
| 0 | 1 |
2023-06-17 04:55:33.734000
|
https://github.com/shenzy08/PDO-s3DCNN
| 3 |
Pdo-s3dcnns: Partial differential operator based steerable 3d cnns
|
https://scholar.google.com/scholar?cluster=7127988507569489900&hl=en&as_sdt=0,24
| 1 | 2,022 |
Staged Training for Transformer Language Models
| 3 |
icml
| 1 | 1 |
2023-06-17 04:55:33.941000
|
https://github.com/allenai/staged-training
| 19 |
Staged training for transformer language models
|
https://scholar.google.com/scholar?cluster=4204701598187830659&hl=en&as_sdt=0,5
| 5 | 2,022 |
Adversarial Masking for Self-Supervised Learning
| 32 |
icml
| 5 | 2 |
2023-06-17 04:55:34.147000
|
https://github.com/yugeten/adios
| 50 |
Adversarial masking for self-supervised learning
|
https://scholar.google.com/scholar?cluster=3881185449721325576&hl=en&as_sdt=0,5
| 3 | 2,022 |
Visual Attention Emerges from Recurrent Sparse Reconstruction
| 4 |
icml
| 2 | 0 |
2023-06-17 04:55:34.353000
|
https://github.com/bfshi/vars
| 24 |
Visual attention emerges from recurrent sparse reconstruction
|
https://scholar.google.com/scholar?cluster=626547526031635836&hl=en&as_sdt=0,44
| 1 | 2,022 |
Robust Group Synchronization via Quadratic Programming
| 1 |
icml
| 0 | 1 |
2023-06-17 04:55:34.559000
|
https://github.com/colewyeth/desc
| 6 |
Robust Group Synchronization via Quadratic Programming
|
https://scholar.google.com/scholar?cluster=14329242327668843280&hl=en&as_sdt=0,39
| 3 | 2,022 |
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets
| 3 |
icml
| 1 | 0 |
2023-06-17 04:55:34.764000
|
https://github.com/shnitzer/les-distance
| 4 |
Log-euclidean signatures for intrinsic distances between unaligned datasets
|
https://scholar.google.com/scholar?cluster=528448898197574004&hl=en&as_sdt=0,24
| 1 | 2,022 |
Demystifying the Adversarial Robustness of Random Transformation Defenses
| 7 |
icml
| 0 | 0 |
2023-06-17 04:55:34.970000
|
https://github.com/wagner-group/demystify-random-transform
| 5 |
Demystifying the adversarial robustness of random transformation defenses
|
https://scholar.google.com/scholar?cluster=6394427111079703523&hl=en&as_sdt=0,23
| 1 | 2,022 |
Communicating via Markov Decision Processes
| 4 |
icml
| 0 | 3 |
2023-06-17 04:55:35.176000
|
https://github.com/schroederdewitt/meme
| 1 |
Communicating via Markov Decision Processes
|
https://scholar.google.com/scholar?cluster=1909863582927997201&hl=en&as_sdt=0,5
| 3 | 2,022 |
The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks
| 3 |
icml
| 1 | 0 |
2023-06-17 04:55:35.381000
|
https://github.com/ideaslabut/multivariate-community-hawkes
| 1 |
The multivariate community hawkes model for dependent relational events in continuous-time networks
|
https://scholar.google.com/scholar?cluster=16117758994538292993&hl=en&as_sdt=0,33
| 3 | 2,022 |
A General Recipe for Likelihood-free Bayesian Optimization
| 8 |
icml
| 2 | 0 |
2023-06-17 04:55:35.587000
|
https://github.com/lfbo-ml/lfbo
| 39 |
A general recipe for likelihood-free Bayesian optimization
|
https://scholar.google.com/scholar?cluster=2199690906597156790&hl=en&as_sdt=0,37
| 3 | 2,022 |
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
| 15 |
icml
| 271 | 7 |
2023-06-17 04:55:35.793000
|
https://github.com/huawei-noah/hebo
| 1,285 |
Sauté rl: Almost surely safe reinforcement learning using state augmentation
|
https://scholar.google.com/scholar?cluster=12545517423097788852&hl=en&as_sdt=0,22
| 130 | 2,022 |
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
| 24 |
icml
| 15 | 1 |
2023-06-17 04:55:35.999000
|
https://github.com/samuelstanton/lambo
| 50 |
Accelerating bayesian optimization for biological sequence design with denoising autoencoders
|
https://scholar.google.com/scholar?cluster=2506639909996415595&hl=en&as_sdt=0,33
| 2 | 2,022 |
3D Infomax improves GNNs for Molecular Property Prediction
| 66 |
icml
| 29 | 4 |
2023-06-17 04:55:36.206000
|
https://github.com/hannesstark/3dinfomax
| 116 |
3d infomax improves gnns for molecular property prediction
|
https://scholar.google.com/scholar?cluster=18195860750409632321&hl=en&as_sdt=0,5
| 3 | 2,022 |
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
| 83 |
icml
| 99 | 5 |
2023-06-17 04:55:36.413000
|
https://github.com/HannesStark/EquiBind
| 397 |
Equibind: Geometric deep learning for drug binding structure prediction
|
https://scholar.google.com/scholar?cluster=2579310543705352041&hl=en&as_sdt=0,5
| 9 | 2,022 |
Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks
| 6 |
icml
| 4 | 1 |
2023-06-17 04:55:36.620000
|
https://github.com/LukasStruppek/Plug-and-Play-Attacks
| 16 |
Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks
|
https://scholar.google.com/scholar?cluster=10382805845190184141&hl=en&as_sdt=0,20
| 2 | 2,022 |
MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection
| 10 |
icml
| 32 | 9 |
2023-06-17 04:55:36.826000
|
https://github.com/alibaba/lightweight-neural-architecture-search
| 266 |
Mae-det: Revisiting maximum entropy principle in zero-shot nas for efficient object detection
|
https://scholar.google.com/scholar?cluster=9429584722885379910&hl=en&as_sdt=0,33
| 10 | 2,022 |
Out-of-Distribution Detection with Deep Nearest Neighbors
| 79 |
icml
| 14 | 1 |
2023-06-17 04:55:37.032000
|
https://github.com/deeplearning-wisc/knn-ood
| 118 |
Out-of-distribution detection with deep nearest neighbors
|
https://scholar.google.com/scholar?cluster=8587930909818673494&hl=en&as_sdt=0,33
| 2 | 2,022 |
Black-Box Tuning for Language-Model-as-a-Service
| 52 |
icml
| 28 | 4 |
2023-06-17 04:55:37.248000
|
https://github.com/txsun1997/black-box-tuning
| 223 |
Black-box tuning for language-model-as-a-service
|
https://scholar.google.com/scholar?cluster=6566630989334663783&hl=en&as_sdt=0,22
| 7 | 2,022 |
Causal Imitation Learning under Temporally Correlated Noise
| 13 |
icml
| 0 | 0 |
2023-06-17 04:55:37.461000
|
https://github.com/gkswamy98/causal_il
| 6 |
Causal imitation learning under temporally correlated noise
|
https://scholar.google.com/scholar?cluster=3778588231646817630&hl=en&as_sdt=0,5
| 2 | 2,022 |
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization
| 10 |
icml
| 14 | 1 |
2023-06-17 04:55:37.667000
|
https://github.com/sony/sqvae
| 132 |
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization
|
https://scholar.google.com/scholar?cluster=13353459274510421570&hl=en&as_sdt=0,10
| 6 | 2,022 |
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources
| 18 |
icml
| 1 | 0 |
2023-06-17 04:55:37.872000
|
https://github.com/ellenxtan/ifedtree
| 8 |
A tree-based model averaging approach for personalized treatment effect estimation from heterogeneous data sources
|
https://scholar.google.com/scholar?cluster=602189476639254582&hl=en&as_sdt=0,5
| 3 | 2,022 |
Rethinking Graph Neural Networks for Anomaly Detection
| 24 |
icml
| 20 | 0 |
2023-06-17 04:55:38.077000
|
https://github.com/squareroot3/rethinking-anomaly-detection
| 118 |
Rethinking graph neural networks for anomaly detection
|
https://scholar.google.com/scholar?cluster=15800828162221381866&hl=en&as_sdt=0,33
| 1 | 2,022 |
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning
| 15 |
icml
| 9 | 5 |
2023-06-17 04:55:38.284000
|
https://github.com/wizard1203/vhl
| 29 |
Virtual homogeneity learning: Defending against data heterogeneity in federated learning
|
https://scholar.google.com/scholar?cluster=5551753342557173221&hl=en&as_sdt=0,34
| 2 | 2,022 |
FedNest: Federated Bilevel, Minimax, and Compositional Optimization
| 23 |
icml
| 1 | 0 |
2023-06-17 04:55:38.491000
|
https://github.com/ucr-optml/FedNest
| 8 |
FedNest: Federated bilevel, minimax, and compositional optimization
|
https://scholar.google.com/scholar?cluster=7138561365880400777&hl=en&as_sdt=0,24
| 2 | 2,022 |
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood
| 8 |
icml
| 1 | 1 |
2023-06-17 04:55:38.697000
|
https://github.com/opium-sh/lidl
| 7 |
Lidl: Local intrinsic dimension estimation using approximate likelihood
|
https://scholar.google.com/scholar?cluster=9636618006452252616&hl=en&as_sdt=0,11
| 3 | 2,022 |
Quantifying and Learning Linear Symmetry-Based Disentanglement
| 7 |
icml
| 0 | 0 |
2023-06-17 04:55:38.902000
|
https://github.com/luis-armando-perez-rey/lsbd-vae
| 0 |
Quantifying and learning linear symmetry-based disentanglement
|
https://scholar.google.com/scholar?cluster=11951723712936247797&hl=en&as_sdt=0,33
| 2 | 2,022 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.