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
|
|---|---|---|---|---|---|---|---|---|---|---|---|
Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing
| 8 |
icml
| 2 | 0 |
2023-06-17 04:54:37.037000
|
https://github.com/mli-lab/ttt_for_deep_learning_cs
| 9 |
Test-time training can close the natural distribution shift performance gap in deep learning based compressed sensing
|
https://scholar.google.com/scholar?cluster=17586372982715627644&hl=en&as_sdt=0,33
| 1 | 2,022 |
Knowledge Base Question Answering by Case-based Reasoning over Subgraphs
| 19 |
icml
| 5 | 4 |
2023-06-17 04:54:37.243000
|
https://github.com/rajarshd/cbr-subg
| 28 |
Knowledge base question answering by case-based reasoning over subgraphs
|
https://scholar.google.com/scholar?cluster=9521902592444277767&hl=en&as_sdt=0,33
| 4 | 2,022 |
Robust Multi-Objective Bayesian Optimization Under Input Noise
| 15 |
icml
| 1 | 0 |
2023-06-17 04:54:37.448000
|
https://github.com/facebookresearch/robust_mobo
| 36 |
Robust multi-objective bayesian optimization under input noise
|
https://scholar.google.com/scholar?cluster=14538783621300673718&hl=en&as_sdt=0,5
| 13 | 2,022 |
Attentional Meta-learners for Few-shot Polythetic Classification
| 1 |
icml
| 1 | 0 |
2023-06-17 04:54:37.654000
|
https://github.com/rvinas/polythetic_metalearning
| 7 |
Attentional Meta-learners for Few-shot Polythetic Classification
|
https://scholar.google.com/scholar?cluster=5360824455580624680&hl=en&as_sdt=0,47
| 3 | 2,022 |
Adversarial Vulnerability of Randomized Ensembles
| 1 |
icml
| 1 | 0 |
2023-06-17 04:54:37.859000
|
https://github.com/hsndbk4/arc
| 9 |
Adversarial Vulnerability of Randomized Ensembles
|
https://scholar.google.com/scholar?cluster=2408757977511355426&hl=en&as_sdt=0,5
| 1 | 2,022 |
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization
| 4 |
icml
| 4 | 0 |
2023-06-17 04:54:38.066000
|
https://github.com/gbdl/bbi
| 5 |
Born-Infeld (BI) for AI: energy-conserving descent (ECD) for optimization
|
https://scholar.google.com/scholar?cluster=11927103073322066327&hl=en&as_sdt=0,44
| 2 | 2,022 |
Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass
| 7 |
icml
| 2 | 0 |
2023-06-17 04:54:38.271000
|
https://github.com/giorgiad/pepita
| 16 |
Error-driven input modulation: solving the credit assignment problem without a backward pass
|
https://scholar.google.com/scholar?cluster=12440766337737848620&hl=en&as_sdt=0,5
| 1 | 2,022 |
DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations
| 20 |
icml
| 4 | 0 |
2023-06-17 04:54:38.477000
|
https://github.com/fdeng18/dreamer-pro
| 26 |
Dreamerpro: Reconstruction-free model-based reinforcement learning with prototypical representations
|
https://scholar.google.com/scholar?cluster=11064573461444670693&hl=en&as_sdt=0,34
| 1 | 2,022 |
NeuralEF: Deconstructing Kernels by Deep Neural Networks
| 9 |
icml
| 1 | 0 |
2023-06-17 04:54:38.683000
|
https://github.com/thudzj/neuraleigenfunction
| 10 |
Neuralef: Deconstructing kernels by deep neural networks
|
https://scholar.google.com/scholar?cluster=14961387103388663924&hl=en&as_sdt=0,47
| 2 | 2,022 |
Generalization and Robustness Implications in Object-Centric Learning
| 20 |
icml
| 2 | 0 |
2023-06-17 04:54:38.889000
|
https://github.com/addtt/object-centric-library
| 61 |
Generalization and robustness implications in object-centric learning
|
https://scholar.google.com/scholar?cluster=9362373326387424526&hl=en&as_sdt=0,33
| 3 | 2,022 |
Fair Generalized Linear Models with a Convex Penalty
| 1 |
icml
| 1 | 1 |
2023-06-17 04:54:39.095000
|
https://github.com/hyungrok-do/fair-glm-cvx
| 0 |
Fair Generalized Linear Models with a Convex Penalty
|
https://scholar.google.com/scholar?cluster=11693304205339987181&hl=en&as_sdt=0,33
| 3 | 2,022 |
On the Adversarial Robustness of Causal Algorithmic Recourse
| 28 |
icml
| 0 | 0 |
2023-06-17 04:54:39.300000
|
https://github.com/ricardodominguez/adversariallyrobustrecourse
| 5 |
On the adversarial robustness of causal algorithmic recourse
|
https://scholar.google.com/scholar?cluster=16011924534958641945&hl=en&as_sdt=0,14
| 1 | 2,022 |
Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks
| 4 |
icml
| 1 | 0 |
2023-06-17 04:54:39.505000
|
https://github.com/RunpeiDong/DGMS
| 5 |
Finding the task-optimal low-bit sub-distribution in deep neural networks
|
https://scholar.google.com/scholar?cluster=7264575101488982108&hl=en&as_sdt=0,47
| 2 | 2,022 |
PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs
| 2 |
icml
| 0 | 0 |
2023-06-17 04:54:39.711000
|
https://github.com/zehao-dong/pace
| 7 |
PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs
|
https://scholar.google.com/scholar?cluster=11354614986119464774&hl=en&as_sdt=0,5
| 1 | 2,022 |
Adapting to Mixing Time in Stochastic Optimization with Markovian Data
| 8 |
icml
| 8 | 0 |
2023-06-17 04:54:39.916000
|
https://github.com/Rondorf/BOReL
| 20 |
Adapting to mixing time in stochastic optimization with markovian data
|
https://scholar.google.com/scholar?cluster=4133641935390571413&hl=en&as_sdt=0,45
| 3 | 2,022 |
TACTiS: Transformer-Attentional Copulas for Time Series
| 11 |
icml
| 11 | 3 |
2023-06-17 04:54:40.121000
|
https://github.com/ServiceNow/tactis
| 72 |
Tactis: Transformer-attentional copulas for time series
|
https://scholar.google.com/scholar?cluster=5604382526172400005&hl=en&as_sdt=0,33
| 8 | 2,022 |
Learning Iterative Reasoning through Energy Minimization
| 4 |
icml
| 6 | 4 |
2023-06-17 04:54:40.327000
|
https://github.com/yilundu/irem_code_release
| 38 |
Learning iterative reasoning through energy minimization
|
https://scholar.google.com/scholar?cluster=1554477033097529382&hl=en&as_sdt=0,7
| 3 | 2,022 |
SE(3) Equivariant Graph Neural Networks with Complete Local Frames
| 10 |
icml
| 6 | 1 |
2023-06-17 04:54:40.534000
|
https://github.com/mouthful/ClofNet
| 11 |
SE (3) Equivariant Graph Neural Networks with Complete Local Frames
|
https://scholar.google.com/scholar?cluster=14602440346377958112&hl=en&as_sdt=0,33
| 2 | 2,022 |
A Context-Integrated Transformer-Based Neural Network for Auction Design
| 10 |
icml
| 1 | 0 |
2023-06-17 04:54:40.739000
|
https://github.com/zjduan/CITransNet
| 10 |
A context-integrated transformer-based neural network for auction design
|
https://scholar.google.com/scholar?cluster=9850607820011561614&hl=en&as_sdt=0,5
| 1 | 2,022 |
From data to functa: Your data point is a function and you can treat it like one
| 33 |
icml
| 4 | 3 |
2023-06-17 04:54:40.944000
|
https://github.com/deepmind/functa
| 101 |
From data to functa: Your data point is a function and you can treat it like one
|
https://scholar.google.com/scholar?cluster=4550089326904681331&hl=en&as_sdt=0,39
| 8 | 2,022 |
On the Difficulty of Defending Self-Supervised Learning against Model Extraction
| 7 |
icml
| 0 | 0 |
2023-06-17 04:54:41.150000
|
https://github.com/cleverhans-lab/ssl-attacks-defenses
| 1 |
On the difficulty of defending self-supervised learning against model extraction
|
https://scholar.google.com/scholar?cluster=16145224211258754535&hl=en&as_sdt=0,33
| 1 | 2,022 |
LIMO: Latent Inceptionism for Targeted Molecule Generation
| 8 |
icml
| 14 | 9 |
2023-06-17 04:54:41.356000
|
https://github.com/rose-stl-lab/limo
| 44 |
LIMO: Latent Inceptionism for Targeted Molecule Generation
|
https://scholar.google.com/scholar?cluster=12167942813454300503&hl=en&as_sdt=0,10
| 3 | 2,022 |
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
| 5 |
icml
| 1 | 1 |
2023-06-17 04:54:41.561000
|
https://github.com/aelgabli/fednew
| 9 |
FedNew: A communication-efficient and privacy-preserving Newton-type method for federated learning
|
https://scholar.google.com/scholar?cluster=13605239667986344129&hl=en&as_sdt=0,5
| 1 | 2,022 |
For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria
| 1 |
icml
| 0 | 0 |
2023-06-17 04:54:41.767000
|
https://github.com/scottemmons/coordination
| 0 |
For learning in symmetric teams, local optima are global nash equilibria
|
https://scholar.google.com/scholar?cluster=16109782432543935692&hl=en&as_sdt=0,33
| 2 | 2,022 |
Towards Scaling Difference Target Propagation by Learning Backprop Targets
| 11 |
icml
| 0 | 0 |
2023-06-17 04:54:41.973000
|
https://github.com/bptargetdtp/scalabledtp
| 1 |
Towards scaling difference target propagation by learning backprop targets
|
https://scholar.google.com/scholar?cluster=16976057052458549832&hl=en&as_sdt=0,5
| 2 | 2,022 |
Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information
| 18 |
icml
| 8 | 0 |
2023-06-17 04:54:42.180000
|
https://github.com/kawine/dataset_difficulty
| 58 |
Understanding Dataset Difficulty with $\mathcalV $-Usable Information
|
https://scholar.google.com/scholar?cluster=446878521601081307&hl=en&as_sdt=0,5
| 1 | 2,022 |
Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning
| 36 |
icml
| 9 | 1 |
2023-06-17 04:54:42.386000
|
https://github.com/google-research/head2toe
| 71 |
Head2toe: Utilizing intermediate representations for better transfer learning
|
https://scholar.google.com/scholar?cluster=12027550380073751806&hl=en&as_sdt=0,33
| 6 | 2,022 |
Variational Sparse Coding with Learned Thresholding
| 0 |
icml
| 1 | 0 |
2023-06-17 04:54:42.593000
|
https://github.com/kfallah/variational-sparse-coding
| 7 |
Variational Sparse Coding with Learned Thresholding
|
https://scholar.google.com/scholar?cluster=10401057138019982209&hl=en&as_sdt=0,43
| 2 | 2,022 |
Training Discrete Deep Generative Models via Gapped Straight-Through Estimator
| 4 |
icml
| 0 | 0 |
2023-06-17 04:54:42.798000
|
https://github.com/chijames/gst
| 8 |
Training Discrete Deep Generative Models via Gapped Straight-Through Estimator
|
https://scholar.google.com/scholar?cluster=3212785124198988357&hl=en&as_sdt=0,50
| 1 | 2,022 |
DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck
| 16 |
icml
| 2 | 1 |
2023-06-17 04:54:43.003000
|
https://github.com/BU-DEPEND-Lab/DRIBO
| 4 |
Dribo: Robust deep reinforcement learning via multi-view information bottleneck
|
https://scholar.google.com/scholar?cluster=17795910493641193453&hl=en&as_sdt=0,10
| 1 | 2,022 |
Variational Wasserstein gradient flow
| 20 |
icml
| 0 | 0 |
2023-06-17 04:54:43.209000
|
https://github.com/sbyebss/variational_wgf
| 9 |
Variational wasserstein gradient flow
|
https://scholar.google.com/scholar?cluster=4247639090058922494&hl=en&as_sdt=0,34
| 1 | 2,022 |
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)
| 38 |
icml
| 1 | 0 |
2023-06-17 04:54:43.428000
|
https://github.com/mlfoundations/imagenet-captions
| 33 |
Data determines distributional robustness in contrastive language image pre-training (clip)
|
https://scholar.google.com/scholar?cluster=12568254041342889008&hl=en&as_sdt=0,5
| 5 | 2,022 |
An Equivalence Between Data Poisoning and Byzantine Gradient Attacks
| 5 |
icml
| 1 | 0 |
2023-06-17 04:54:43.634000
|
https://github.com/lpd-epfl/attack_equivalence
| 1 |
An equivalence between data poisoning and byzantine gradient attacks
|
https://scholar.google.com/scholar?cluster=15814948581438408162&hl=en&as_sdt=0,5
| 1 | 2,022 |
Investigating Generalization by Controlling Normalized Margin
| 3 |
icml
| 0 | 0 |
2023-06-17 04:54:43.839000
|
https://github.com/alexfarhang/margin
| 1 |
Investigating Generalization by Controlling Normalized Margin
|
https://scholar.google.com/scholar?cluster=715638377527231014&hl=en&as_sdt=0,34
| 1 | 2,022 |
Private frequency estimation via projective geometry
| 6 |
icml
| 0 | 0 |
2023-06-17 04:54:44.044000
|
https://github.com/minilek/private_frequency_oracles
| 3 |
Private frequency estimation via projective geometry
|
https://scholar.google.com/scholar?cluster=5605547034926514625&hl=en&as_sdt=0,33
| 1 | 2,022 |
Coordinated Double Machine Learning
| 0 |
icml
| 1 | 0 |
2023-06-17 04:54:44.250000
|
https://github.com/nitaifingerhut/c-dml
| 3 |
Coordinated Double Machine Learning
|
https://scholar.google.com/scholar?cluster=3098806630799952921&hl=en&as_sdt=0,10
| 2 | 2,022 |
Conformal Prediction Sets with Limited False Positives
| 4 |
icml
| 0 | 1 |
2023-06-17 04:54:44.457000
|
https://github.com/ajfisch/conformal-fp
| 0 |
Conformal prediction sets with limited false positives
|
https://scholar.google.com/scholar?cluster=3023340906965759657&hl=en&as_sdt=0,36
| 1 | 2,022 |
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
| 1 |
icml
| 1 | 0 |
2023-06-17 04:54:44.662000
|
https://github.com/benevolentai/comp
| 7 |
Contrastive mixture of posteriors for counterfactual inference, data integration and fairness
|
https://scholar.google.com/scholar?cluster=7874050188706328624&hl=en&as_sdt=0,5
| 3 | 2,022 |
A Neural Tangent Kernel Perspective of GANs
| 13 |
icml
| 2 | 0 |
2023-06-17 04:54:44.869000
|
https://github.com/emited/gantk2
| 13 |
A neural tangent kernel perspective of gans
|
https://scholar.google.com/scholar?cluster=4606779800346786718&hl=en&as_sdt=0,5
| 4 | 2,022 |
SPDY: Accurate Pruning with Speedup Guarantees
| 7 |
icml
| 4 | 3 |
2023-06-17 04:54:45.075000
|
https://github.com/ist-daslab/spdy
| 11 |
SPDY: Accurate pruning with speedup guarantees
|
https://scholar.google.com/scholar?cluster=9481477632006628831&hl=en&as_sdt=0,32
| 5 | 2,022 |
Scaling Structured Inference with Randomization
| 2 |
icml
| 3 | 0 |
2023-06-17 04:54:45.280000
|
https://github.com/franxyao/rdp
| 13 |
Scaling structured inference with randomization
|
https://scholar.google.com/scholar?cluster=13234676438098295868&hl=en&as_sdt=0,38
| 2 | 2,022 |
DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks
| 5 |
icml
| 1 | 1 |
2023-06-17 04:54:45.488000
|
https://github.com/rice-eic/depthshrinker
| 36 |
DepthShrinker: a new compression paradigm towards boosting real-hardware efficiency of compact neural networks
|
https://scholar.google.com/scholar?cluster=13003128521759488248&hl=en&as_sdt=0,33
| 10 | 2,022 |
$p$-Laplacian Based Graph Neural Networks
| 7 |
icml
| 2 | 0 |
2023-06-17 04:54:45.693000
|
https://github.com/guoji-fu/pgnns
| 21 |
-Laplacian Based Graph Neural Networks
|
https://scholar.google.com/scholar?cluster=15123165040444629585&hl=en&as_sdt=0,33
| 2 | 2,022 |
Generalizing Gaussian Smoothing for Random Search
| 2 |
icml
| 0 | 0 |
2023-06-17 04:54:45.898000
|
https://github.com/isl-org/generalized-smoothing
| 3 |
Generalizing Gaussian Smoothing for Random Search
|
https://scholar.google.com/scholar?cluster=2545306041243695019&hl=en&as_sdt=0,5
| 4 | 2,022 |
Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems
| 2 |
icml
| 1 | 0 |
2023-06-17 04:54:46.105000
|
https://github.com/wi-pi/rethinking-image-scaling-attacks
| 3 |
Rethinking image-scaling attacks: The interplay between vulnerabilities in machine learning systems
|
https://scholar.google.com/scholar?cluster=9730023948978190760&hl=en&as_sdt=0,11
| 2 | 2,022 |
Lazy Estimation of Variable Importance for Large Neural Networks
| 1 |
icml
| 0 | 0 |
2023-06-17 04:54:46.313000
|
https://github.com/willett-group/lazyvi
| 0 |
Lazy Estimation of Variable Importance for Large Neural Networks
|
https://scholar.google.com/scholar?cluster=11646154414177168250&hl=en&as_sdt=0,3
| 2 | 2,022 |
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack
| 3 |
icml
| 2 | 0 |
2023-06-17 04:54:46.526000
|
https://github.com/sjtubrian/mm-attack
| 4 |
Fast and reliable evaluation of adversarial robustness with minimum-margin attack
|
https://scholar.google.com/scholar?cluster=16577119936016409064&hl=en&as_sdt=0,5
| 1 | 2,022 |
Value Function based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems
| 9 |
icml
| 3 | 0 |
2023-06-17 04:54:46.731000
|
https://github.com/sustech-optimization/vf-idca
| 2 |
Value function based difference-of-convex algorithm for bilevel hyperparameter selection problems
|
https://scholar.google.com/scholar?cluster=5559492833861486776&hl=en&as_sdt=0,10
| 1 | 2,022 |
Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization
| 1 |
icml
| 1 | 1 |
2023-06-17 04:54:46.937000
|
https://github.com/xianggao1102/learning-to-incorporate-texture-saliency-adaptive-attention-to-image-cartoonization
| 4 |
Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization
|
https://scholar.google.com/scholar?cluster=11484326183315995757&hl=en&as_sdt=0,33
| 1 | 2,022 |
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
| 3 |
icml
| 0 | 2 |
2023-06-17 04:54:47.142000
|
https://github.com/garcinc/noised-topk
| 10 |
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
|
https://scholar.google.com/scholar?cluster=16642060329776900644&hl=en&as_sdt=0,47
| 2 | 2,022 |
A Functional Information Perspective on Model Interpretation
| 1 |
icml
| 0 | 0 |
2023-06-17 04:54:47.347000
|
https://github.com/nitaytech/functionalexplanation
| 5 |
A Functional Information Perspective on Model Interpretation
|
https://scholar.google.com/scholar?cluster=5647868257497386951&hl=en&as_sdt=0,33
| 1 | 2,022 |
Inducing Causal Structure for Interpretable Neural Networks
| 20 |
icml
| 0 | 0 |
2023-06-17 04:54:47.554000
|
https://github.com/frankaging/interchange-intervention-training
| 7 |
Inducing causal structure for interpretable neural networks
|
https://scholar.google.com/scholar?cluster=3318078853003855419&hl=en&as_sdt=0,5
| 2 | 2,022 |
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
| 9 |
icml
| 5 | 0 |
2023-06-17 04:54:47.760000
|
https://github.com/jmaces/aapm-ct-challenge
| 34 |
Near-exact recovery for tomographic inverse problems via deep learning
|
https://scholar.google.com/scholar?cluster=10012619344494620426&hl=en&as_sdt=0,5
| 3 | 2,022 |
Equivariance versus Augmentation for Spherical Images
| 8 |
icml
| 1 | 0 |
2023-06-17 04:54:47.966000
|
https://github.com/janegerken/sem_seg_s2cnn
| 2 |
Equivariance versus augmentation for spherical images
|
https://scholar.google.com/scholar?cluster=2388075100052458630&hl=en&as_sdt=0,33
| 0 | 2,022 |
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations
| 1 |
icml
| 0 | 1 |
2023-06-17 04:54:48.170000
|
https://github.com/youranonymousefriend/plugininversion
| 9 |
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations
|
https://scholar.google.com/scholar?cluster=3783911125052785325&hl=en&as_sdt=0,5
| 1 | 2,022 |
SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation
| 2 |
icml
| 0 | 0 |
2023-06-17 04:54:48.377000
|
https://github.com/georgosgeorgos/hierarchical-few-shot-generative-models
| 10 |
Scha-vae: Hierarchical context aggregation for few-shot generation
|
https://scholar.google.com/scholar?cluster=18154128388289892262&hl=en&as_sdt=0,23
| 1 | 2,022 |
RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression
| 8 |
icml
| 5 | 2 |
2023-06-17 04:54:48.585000
|
https://github.com/BorealisAI/ranksim-imbalanced-regression
| 27 |
RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression
|
https://scholar.google.com/scholar?cluster=2649008384099907500&hl=en&as_sdt=0,5
| 2 | 2,022 |
Causal Inference Through the Structural Causal Marginal Problem
| 6 |
icml
| 3 | 0 |
2023-06-17 04:54:48.791000
|
https://github.com/lgresele/structural-causal-marginal
| 2 |
Causal inference through the structural causal marginal problem
|
https://scholar.google.com/scholar?cluster=2256399104999533783&hl=en&as_sdt=0,47
| 1 | 2,022 |
Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics
| 3 |
icml
| 1 | 2 |
2023-06-17 04:54:48.997000
|
https://github.com/welch-lab/velovae
| 21 |
Variational mixtures of ODEs for inferring cellular gene expression dynamics
|
https://scholar.google.com/scholar?cluster=5570506012304975998&hl=en&as_sdt=0,47
| 5 | 2,022 |
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity
| 7 |
icml
| 1 | 0 |
2023-06-17 04:54:49.202000
|
https://github.com/GuanSuns/ASGRL
| 11 |
Leveraging approximate symbolic models for reinforcement learning via skill diversity
|
https://scholar.google.com/scholar?cluster=9607066569965060600&hl=en&as_sdt=0,29
| 1 | 2,022 |
Bounding Training Data Reconstruction in Private (Deep) Learning
| 14 |
icml
| 0 | 0 |
2023-06-17 04:54:49.411000
|
https://github.com/facebookresearch/bounding_data_reconstruction
| 10 |
Bounding training data reconstruction in private (deep) learning
|
https://scholar.google.com/scholar?cluster=3008455373482985083&hl=en&as_sdt=0,23
| 4 | 2,022 |
NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks
| 8 |
icml
| 2 | 0 |
2023-06-17 04:54:49.617000
|
https://github.com/burakgurbuz97/nispa
| 15 |
Nispa: Neuro-inspired stability-plasticity adaptation for continual learning in sparse networks
|
https://scholar.google.com/scholar?cluster=17073314745146797398&hl=en&as_sdt=0,5
| 2 | 2,022 |
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets
| 25 |
icml
| 4 | 0 |
2023-06-17 04:54:49.823000
|
https://github.com/avihu111/typiclust
| 44 |
Active learning on a budget: Opposite strategies suit high and low budgets
|
https://scholar.google.com/scholar?cluster=7933856557848734665&hl=en&as_sdt=0,36
| 4 | 2,022 |
You Only Cut Once: Boosting Data Augmentation with a Single Cut
| 9 |
icml
| 10 | 3 |
2023-06-17 04:54:50.032000
|
https://github.com/junlinhan/yoco
| 93 |
You only cut once: Boosting data augmentation with a single cut
|
https://scholar.google.com/scholar?cluster=501111593877482032&hl=en&as_sdt=0,24
| 3 | 2,022 |
Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes
| 1 |
icml
| 0 | 0 |
2023-06-17 04:54:50.238000
|
https://github.com/insuhan/ndpp-mcmc-sampling
| 0 |
Scalable mcmc sampling for nonsymmetric determinantal point processes
|
https://scholar.google.com/scholar?cluster=280717695600419200&hl=en&as_sdt=0,5
| 1 | 2,022 |
Adversarial Attacks on Gaussian Process Bandits
| 2 |
icml
| 0 | 0 |
2023-06-17 04:54:50.443000
|
https://github.com/eric-vader/attack-bo
| 1 |
Adversarial attacks on Gaussian process bandits
|
https://scholar.google.com/scholar?cluster=13292319437654740768&hl=en&as_sdt=0,5
| 2 | 2,022 |
Temporal Difference Learning for Model Predictive Control
| 35 |
icml
| 40 | 1 |
2023-06-17 04:54:50.650000
|
https://github.com/nicklashansen/tdmpc
| 201 |
Temporal difference learning for model predictive control
|
https://scholar.google.com/scholar?cluster=10762661949285432757&hl=en&as_sdt=0,34
| 4 | 2,022 |
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses
| 13 |
icml
| 1 | 0 |
2023-06-17 04:54:50.855000
|
https://github.com/logan-stapleton/strategic-instrumental-variable-regression
| 0 |
Strategic instrumental variable regression: Recovering causal relationships from strategic responses
|
https://scholar.google.com/scholar?cluster=5426296166892217767&hl=en&as_sdt=0,29
| 2 | 2,022 |
General-purpose, long-context autoregressive modeling with Perceiver AR
| 22 |
icml
| 18 | 16 |
2023-06-17 04:54:51.061000
|
https://github.com/google-research/perceiver-ar
| 202 |
General-purpose, long-context autoregressive modeling with perceiver ar
|
https://scholar.google.com/scholar?cluster=1307821423265105144&hl=en&as_sdt=0,1
| 12 | 2,022 |
On Distribution Shift in Learning-based Bug Detectors
| 10 |
icml
| 4 | 2 |
2023-06-17 04:54:51.266000
|
https://github.com/eth-sri/learning-real-bug-detector
| 12 |
On distribution shift in learning-based bug detectors
|
https://scholar.google.com/scholar?cluster=16187870824460798751&hl=en&as_sdt=0,1
| 8 | 2,022 |
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
| 6 |
icml
| 8 | 0 |
2023-06-17 04:54:51.472000
|
https://github.com/sherylhyx/gnnrank
| 39 |
GNNRank: Learning global rankings from pairwise comparisons via directed graph neural networks
|
https://scholar.google.com/scholar?cluster=4446473441491315248&hl=en&as_sdt=0,5
| 2 | 2,022 |
Sparse Double Descent: Where Network Pruning Aggravates Overfitting
| 7 |
icml
| 1 | 0 |
2023-06-17 04:54:51.677000
|
https://github.com/hezheug/sparse-double-descent
| 14 |
Sparse Double Descent: Where Network Pruning Aggravates Overfitting
|
https://scholar.google.com/scholar?cluster=13575634226332267218&hl=en&as_sdt=0,5
| 2 | 2,022 |
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors
| 2 |
icml
| 0 | 0 |
2023-06-17 04:54:51.883000
|
https://github.com/uds-lsv/premise
| 2 |
Label-descriptive patterns and their application to characterizing classification errors
|
https://scholar.google.com/scholar?cluster=17151062876326396641&hl=en&as_sdt=0,5
| 5 | 2,022 |
NOMU: Neural Optimization-based Model Uncertainty
| 10 |
icml
| 5 | 1 |
2023-06-17 04:54:52.089000
|
https://github.com/marketdesignresearch/NOMU
| 7 |
Nomu: Neural optimization-based model uncertainty
|
https://scholar.google.com/scholar?cluster=17483969048738577269&hl=en&as_sdt=0,39
| 1 | 2,022 |
Scaling Out-of-Distribution Detection for Real-World Settings
| 137 |
icml
| 19 | 0 |
2023-06-17 04:54:52.295000
|
https://github.com/hendrycks/anomaly-seg
| 144 |
Scaling out-of-distribution detection for real-world settings
|
https://scholar.google.com/scholar?cluster=8919172731066658800&hl=en&as_sdt=0,10
| 9 | 2,022 |
Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology
| 0 |
icml
| 0 | 0 |
2023-06-17 04:54:52.501000
|
https://github.com/valentinhofmann/unsupervised_bias
| 0 |
Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology
|
https://scholar.google.com/scholar?cluster=11219729475628655718&hl=en&as_sdt=0,5
| 1 | 2,022 |
Equivariant Diffusion for Molecule Generation in 3D
| 145 |
icml
| 64 | 13 |
2023-06-17 04:54:52.707000
|
https://github.com/ehoogeboom/e3_diffusion_for_molecules
| 260 |
Equivariant diffusion for molecule generation in 3d
|
https://scholar.google.com/scholar?cluster=9412014854490527272&hl=en&as_sdt=0,14
| 7 | 2,022 |
Conditional GANs with Auxiliary Discriminative Classifier
| 7 |
icml
| 4 | 0 |
2023-06-17 04:54:52.912000
|
https://github.com/houliangict/adcgan
| 15 |
Conditional GANs with auxiliary discriminative classifier
|
https://scholar.google.com/scholar?cluster=868024013198158367&hl=en&as_sdt=0,5
| 1 | 2,022 |
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
| 162 |
icml
| 20 | 3 |
2023-06-17 04:54:53.118000
|
https://github.com/huangwl18/language-planner
| 163 |
Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
|
https://scholar.google.com/scholar?cluster=11998123682359381476&hl=en&as_sdt=0,3
| 4 | 2,022 |
Going Deeper into Permutation-Sensitive Graph Neural Networks
| 11 |
icml
| 4 | 0 |
2023-06-17 04:54:53.323000
|
https://github.com/zhongyu1998/pg-gnn
| 20 |
Going Deeper into Permutation-Sensitive Graph Neural Networks
|
https://scholar.google.com/scholar?cluster=14997369349376020515&hl=en&as_sdt=0,5
| 1 | 2,022 |
Directed Acyclic Transformer for Non-Autoregressive Machine Translation
| 15 |
icml
| 10 | 6 |
2023-06-17 04:54:53.529000
|
https://github.com/thu-coai/da-transformer
| 89 |
Directed acyclic transformer for non-autoregressive machine translation
|
https://scholar.google.com/scholar?cluster=12752123369496105828&hl=en&as_sdt=0,33
| 7 | 2,022 |
Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors
| 2 |
icml
| 0 | 0 |
2023-06-17 04:54:53.734000
|
https://github.com/gjhuizing/wsingular
| 7 |
Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors
|
https://scholar.google.com/scholar?cluster=15888088169122917171&hl=en&as_sdt=0,5
| 2 | 2,022 |
Robust Kernel Density Estimation with Median-of-Means principle
| 8 |
icml
| 3 | 3 |
2023-06-17 04:54:53.940000
|
https://github.com/lminvielle/mom-kde
| 6 |
Robust kernel density estimation with median-of-means principle
|
https://scholar.google.com/scholar?cluster=14673811907284819215&hl=en&as_sdt=0,5
| 3 | 2,022 |
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization
| 17 |
icml
| 3 | 0 |
2023-06-17 04:54:54.145000
|
https://github.com/samuro95/prox-pnp
| 4 |
Proximal denoiser for convergent plug-and-play optimization with nonconvex regularization
|
https://scholar.google.com/scholar?cluster=12256965087281375600&hl=en&as_sdt=0,5
| 2 | 2,022 |
LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation
| 4 |
icml
| 2 | 0 |
2023-06-17 04:54:54.350000
|
https://github.com/davidireland3/lense
| 9 |
LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation
|
https://scholar.google.com/scholar?cluster=7267816984726307573&hl=en&as_sdt=0,26
| 3 | 2,022 |
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention
| 12 |
icml
| 1 | 1 |
2023-06-17 04:54:54.555000
|
https://github.com/robertcsordas/linear_layer_as_attention
| 14 |
The dual form of neural networks revisited: Connecting test time predictions to training patterns via spotlights of attention
|
https://scholar.google.com/scholar?cluster=11337857580515349157&hl=en&as_sdt=0,5
| 2 | 2,022 |
A Modern Self-Referential Weight Matrix That Learns to Modify Itself
| 19 |
icml
| 17 | 3 |
2023-06-17 04:54:54.761000
|
https://github.com/idsia/modern-srwm
| 148 |
A modern self-referential weight matrix that learns to modify itself
|
https://scholar.google.com/scholar?cluster=10630456414832460528&hl=en&as_sdt=0,33
| 8 | 2,022 |
A deep convolutional neural network that is invariant to time rescaling
| 2 |
icml
| 0 | 1 |
2023-06-17 04:54:54.967000
|
https://github.com/compmem/SITHCon
| 2 |
A deep convolutional neural network that is invariant to time rescaling
|
https://scholar.google.com/scholar?cluster=731774651536846779&hl=en&as_sdt=0,5
| 4 | 2,022 |
Biological Sequence Design with GFlowNets
| 31 |
icml
| 14 | 7 |
2023-06-17 04:54:55.172000
|
https://github.com/mj10/bioseq-gfn-al
| 51 |
Biological sequence design with gflownets
|
https://scholar.google.com/scholar?cluster=13153301030980981497&hl=en&as_sdt=0,39
| 1 | 2,022 |
Combining Diverse Feature Priors
| 5 |
icml
| 0 | 0 |
2023-06-17 04:54:55.378000
|
https://github.com/MadryLab/copriors
| 7 |
Combining diverse feature priors
|
https://scholar.google.com/scholar?cluster=3431368394631636693&hl=en&as_sdt=0,33
| 5 | 2,022 |
Training Your Sparse Neural Network Better with Any Mask
| 5 |
icml
| 3 | 0 |
2023-06-17 04:54:55.584000
|
https://github.com/vita-group/tost
| 20 |
Training your sparse neural network better with any mask
|
https://scholar.google.com/scholar?cluster=17434761620518064417&hl=en&as_sdt=0,11
| 10 | 2,022 |
Planning with Diffusion for Flexible Behavior Synthesis
| 64 |
icml
| 56 | 8 |
2023-06-17 04:54:55.789000
|
https://github.com/jannerm/diffuser
| 441 |
Planning with diffusion for flexible behavior synthesis
|
https://scholar.google.com/scholar?cluster=17441916079353459921&hl=en&as_sdt=0,44
| 8 | 2,022 |
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection
| 9 |
icml
| 4 | 0 |
2023-06-17 04:54:55.997000
|
https://github.com/vanderschaarlab/hyperimpute
| 94 |
Hyperimpute: Generalized iterative imputation with automatic model selection
|
https://scholar.google.com/scholar?cluster=7345905181972151816&hl=en&as_sdt=0,33
| 3 | 2,022 |
Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization
| 5 |
icml
| 0 | 0 |
2023-06-17 04:54:56.204000
|
https://github.com/adrianjav/impartial-vaes
| 3 |
Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization
|
https://scholar.google.com/scholar?cluster=14600839373536938661&hl=en&as_sdt=0,11
| 1 | 2,022 |
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer
| 9 |
icml
| 4 | 3 |
2023-06-17 04:54:56.452000
|
https://github.com/jiwonjeon9603/maser
| 11 |
Maser: Multi-agent reinforcement learning with subgoals generated from experience replay buffer
|
https://scholar.google.com/scholar?cluster=3511041100939657281&hl=en&as_sdt=0,45
| 2 | 2,022 |
Improving Policy Optimization with Generalist-Specialist Learning
| 5 |
icml
| 0 | 0 |
2023-06-17 04:54:56.658000
|
https://github.com/seanjia/gsl
| 3 |
Improving policy optimization with generalist-specialist learning
|
https://scholar.google.com/scholar?cluster=14525219330814535505&hl=en&as_sdt=0,23
| 1 | 2,022 |
Supervised Off-Policy Ranking
| 6 |
icml
| 1 | 1 |
2023-06-17 04:54:56.864000
|
https://github.com/SOPR-T/SOPR-T
| 5 |
Supervised off-policy ranking
|
https://scholar.google.com/scholar?cluster=12930957527069555602&hl=en&as_sdt=0,10
| 1 | 2,022 |
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
| 47 |
icml
| 17 | 2 |
2023-06-17 04:54:57.069000
|
https://github.com/harryjo97/gdss
| 81 |
Score-based generative modeling of graphs via the system of stochastic differential equations
|
https://scholar.google.com/scholar?cluster=4163972994004543532&hl=en&as_sdt=0,5
| 2 | 2,022 |
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees
| 7 |
icml
| 0 | 0 |
2023-06-17 04:54:57.275000
|
https://github.com/neu-statsml-research/robust-fine-tuning
| 2 |
Robust fine-tuning of deep neural networks with hessian-based generalization guarantees
|
https://scholar.google.com/scholar?cluster=6709344473214339936&hl=en&as_sdt=0,5
| 1 | 2,022 |
Flashlight: Enabling Innovation in Tools for Machine Learning
| 11 |
icml
| 468 | 106 |
2023-06-17 04:54:57.481000
|
https://github.com/flashlight/flashlight
| 4,858 |
Flashlight: Enabling innovation in tools for machine learning
|
https://scholar.google.com/scholar?cluster=13806487547053815832&hl=en&as_sdt=0,5
| 123 | 2,022 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.