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|---|---|---|---|---|---|---|---|---|---|---|---|
Combining Label Propagation and Simple Models out-performs Graph Neural Networks
| 195
|
iclr
| 51
| 5
|
2023-06-18 09:25:32.093000
|
https://github.com/CUAI/CorrectAndSmooth
| 264
|
Combining label propagation and simple models out-performs graph neural networks
|
https://scholar.google.com/scholar?cluster=3392954372444403130&hl=en&as_sdt=0,33
| 9
| 2,021
|
Provably robust classification of adversarial examples with detection
| 21
|
iclr
| 2
| 0
|
2023-06-18 09:25:32.296000
|
https://github.com/boschresearch/robust_classification_with_detection
| 7
|
Provably robust classification of adversarial examples with detection
|
https://scholar.google.com/scholar?cluster=2472207606878267459&hl=en&as_sdt=0,47
| 4
| 2,021
|
Fourier Neural Operator for Parametric Partial Differential Equations
| 853
|
iclr
| 391
| 12
|
2023-06-18 09:25:32.500000
|
https://github.com/zongyi-li/fourier_neural_operator
| 1,306
|
Fourier neural operator for parametric partial differential equations
|
https://scholar.google.com/scholar?cluster=12451804788662635900&hl=en&as_sdt=0,10
| 36
| 2,021
|
Class Normalization for (Continual)? Generalized Zero-Shot Learning
| 26
|
iclr
| 4
| 2
|
2023-06-18 09:25:32.704000
|
https://github.com/universome/czsl
| 34
|
Class normalization for (continual)? generalized zero-shot learning
|
https://scholar.google.com/scholar?cluster=12819058346113139372&hl=en&as_sdt=0,33
| 4
| 2,021
|
Adaptive and Generative Zero-Shot Learning
| 42
|
iclr
| 5
| 2
|
2023-06-18 09:25:32.907000
|
https://github.com/anonmous529/AGZSL
| 16
|
Adaptive and generative zero-shot learning
|
https://scholar.google.com/scholar?cluster=17923480096622740507&hl=en&as_sdt=0,5
| 2
| 2,021
|
Disentangling 3D Prototypical Networks for Few-Shot Concept Learning
| 10
|
iclr
| 0
| 0
|
2023-06-18 09:25:33.113000
|
https://github.com/mihirp1998/Disentangling-3D-Prototypical-Nets
| 10
|
Disentangling 3d prototypical networks for few-shot concept learning
|
https://scholar.google.com/scholar?cluster=3118057905544966050&hl=en&as_sdt=0,5
| 2
| 2,021
|
Anytime Sampling for Autoregressive Models via Ordered Autoencoding
| 11
|
iclr
| 3
| 1
|
2023-06-18 09:25:33.316000
|
https://github.com/Newbeeer/Anytime-Auto-Regressive-Model
| 22
|
Anytime sampling for autoregressive models via ordered autoencoding
|
https://scholar.google.com/scholar?cluster=8874332666353389507&hl=en&as_sdt=0,5
| 2
| 2,021
|
Estimating informativeness of samples with Smooth Unique Information
| 15
|
iclr
| 4
| 0
|
2023-06-18 09:25:33.520000
|
https://github.com/awslabs/aws-cv-unique-information
| 9
|
Estimating informativeness of samples with smooth unique information
|
https://scholar.google.com/scholar?cluster=9537970110591918556&hl=en&as_sdt=0,33
| 3
| 2,021
|
Accurate Learning of Graph Representations with Graph Multiset Pooling
| 80
|
iclr
| 19
| 0
|
2023-06-18 09:25:33.723000
|
https://github.com/JinheonBaek/GMT
| 74
|
Accurate learning of graph representations with graph multiset pooling
|
https://scholar.google.com/scholar?cluster=8033778925255724792&hl=en&as_sdt=0,11
| 2
| 2,021
|
Large Batch Simulation for Deep Reinforcement Learning
| 15
|
iclr
| 5
| 0
|
2023-06-18 09:25:33.931000
|
https://github.com/shacklettbp/bps-nav
| 25
|
Large batch simulation for deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=11450590688187242744&hl=en&as_sdt=0,10
| 3
| 2,021
|
Personalized Federated Learning with First Order Model Optimization
| 153
|
iclr
| 9
| 2
|
2023-06-18 09:25:34.135000
|
https://github.com/NVlabs/FedFomo
| 27
|
Personalized federated learning with first order model optimization
|
https://scholar.google.com/scholar?cluster=7443475779505959951&hl=en&as_sdt=0,43
| 6
| 2,021
|
Knowledge Distillation as Semiparametric Inference
| 17
|
iclr
| 5
| 0
|
2023-06-18 09:25:34.347000
|
https://github.com/microsoft/semiparametric-distillation
| 9
|
Knowledge distillation as semiparametric inference
|
https://scholar.google.com/scholar?cluster=13102643237666737869&hl=en&as_sdt=0,5
| 6
| 2,021
|
Randomized Ensembled Double Q-Learning: Learning Fast Without a Model
| 98
|
iclr
| 19
| 0
|
2023-06-18 09:25:34.563000
|
https://github.com/watchernyu/REDQ
| 114
|
Randomized ensembled double q-learning: Learning fast without a model
|
https://scholar.google.com/scholar?cluster=14970286903447223266&hl=en&as_sdt=0,5
| 5
| 2,021
|
Adapting to Reward Progressivity via Spectral Reinforcement Learning
| 1
|
iclr
| 0
| 0
|
2023-06-18 09:25:34.767000
|
https://github.com/mchldann/SpectralDQN
| 2
|
Adapting to Reward Progressivity via Spectral Reinforcement Learning
|
https://scholar.google.com/scholar?cluster=5001746864239325821&hl=en&as_sdt=0,14
| 1
| 2,021
|
Reset-Free Lifelong Learning with Skill-Space Planning
| 32
|
iclr
| 12
| 1
|
2023-06-18 09:25:34.973000
|
https://github.com/kzl/lifelong_rl
| 88
|
Reset-free lifelong learning with skill-space planning
|
https://scholar.google.com/scholar?cluster=9940357312981411546&hl=en&as_sdt=0,5
| 5
| 2,021
|
Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time
| 0
|
iclr
| 0
| 0
|
2023-06-18 09:25:35.182000
|
https://github.com/chycharlie/robust-bn-faster
| 0
|
Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time
|
https://scholar.google.com/scholar?cluster=15264124860839826525&hl=en&as_sdt=0,11
| 1
| 2,021
|
Teaching Temporal Logics to Neural Networks
| 37
|
iclr
| 2
| 0
|
2023-06-18 09:25:35.386000
|
https://github.com/reactive-systems/deepltl
| 22
|
Teaching temporal logics to neural networks
|
https://scholar.google.com/scholar?cluster=12153070486471346373&hl=en&as_sdt=0,5
| 8
| 2,021
|
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes
| 48
|
iclr
| 2
| 2
|
2023-06-18 09:25:35.590000
|
https://github.com/jakesnell/ove-polya-gamma-gp
| 6
|
Bayesian Few-Shot Classification with One-vs-Each P\'olya-Gamma Augmented Gaussian Processes
|
https://scholar.google.com/scholar?cluster=6827153911215871406&hl=en&as_sdt=0,34
| 2
| 2,021
|
Parameter-Based Value Functions
| 16
|
iclr
| 0
| 0
|
2023-06-18 09:25:35.795000
|
https://github.com/ff93/parameter-based-value-functions
| 4
|
Parameter-based value functions
|
https://scholar.google.com/scholar?cluster=12104932670063298799&hl=en&as_sdt=0,5
| 1
| 2,021
|
Hyperbolic Neural Networks++
| 80
|
iclr
| 6
| 1
|
2023-06-18 09:25:35.998000
|
https://github.com/mil-tokyo/hyperbolic_nn_plusplus
| 50
|
Hyperbolic neural networks++
|
https://scholar.google.com/scholar?cluster=13702563246653838309&hl=en&as_sdt=0,33
| 5
| 2,021
|
Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections
| 10
|
iclr
| 4
| 2
|
2023-06-18 09:25:36.206000
|
https://github.com/tgcsaba/seq2tens
| 26
|
Seq2tens: An efficient representation of sequences by low-rank tensor projections
|
https://scholar.google.com/scholar?cluster=14845817599481722738&hl=en&as_sdt=0,30
| 5
| 2,021
|
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization
| 28
|
iclr
| 11
| 3
|
2023-06-18 09:25:36.416000
|
https://github.com/FOCAL-ICLR/FOCAL-ICLR
| 40
|
Focal: Efficient fully-offline meta-reinforcement learning via distance metric learning and behavior regularization
|
https://scholar.google.com/scholar?cluster=9761035246816366860&hl=en&as_sdt=0,47
| 2
| 2,021
|
Generating Adversarial Computer Programs using Optimized Obfuscations
| 22
|
iclr
| 4
| 2
|
2023-06-18 09:25:36.620000
|
https://github.com/ALFA-group/adversarial-code-generation
| 19
|
Generating adversarial computer programs using optimized obfuscations
|
https://scholar.google.com/scholar?cluster=1001230882267147217&hl=en&as_sdt=0,5
| 4
| 2,021
|
CPR: Classifier-Projection Regularization for Continual Learning
| 36
|
iclr
| 5
| 1
|
2023-06-18 09:25:36.855000
|
https://github.com/csm9493/CPR_CL
| 10
|
CPR: classifier-projection regularization for continual learning
|
https://scholar.google.com/scholar?cluster=17725325187082298099&hl=en&as_sdt=0,14
| 2
| 2,021
|
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images
| 17
|
iclr
| 5
| 1
|
2023-06-18 09:25:37.058000
|
https://github.com/csm9493/GAN2GAN
| 28
|
GAN2GAN: Generative noise learning for blind denoising with single noisy images
|
https://scholar.google.com/scholar?cluster=5021545804729568427&hl=en&as_sdt=0,44
| 3
| 2,021
|
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis
| 9
|
iclr
| 0
| 0
|
2023-06-18 09:25:37.262000
|
https://github.com/zpbao/bowtie_networks
| 0
|
Bowtie networks: Generative modeling for joint few-shot recognition and novel-view synthesis
|
https://scholar.google.com/scholar?cluster=4751463230610145393&hl=en&as_sdt=0,5
| 1
| 2,021
|
Taming GANs with Lookahead-Minmax
| 19
|
iclr
| 7
| 0
|
2023-06-18 09:25:37.465000
|
https://github.com/Chavdarova/LAGAN-Lookahead_Minimax
| 14
|
Taming GANs with lookahead-minmax
|
https://scholar.google.com/scholar?cluster=14906130844734900788&hl=en&as_sdt=0,5
| 4
| 2,021
|
Is Attention Better Than Matrix Decomposition?
| 74
|
iclr
| 20
| 0
|
2023-06-18 09:25:37.668000
|
https://github.com/Gsunshine/Enjoy-Hamburger
| 292
|
Is attention better than matrix decomposition?
|
https://scholar.google.com/scholar?cluster=14362607193647727267&hl=en&as_sdt=0,36
| 8
| 2,021
|
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers
| 79
|
iclr
| 4
| 0
|
2023-06-18 09:25:37.872000
|
https://github.com/kaidixu/LiRPA_Verify
| 15
|
Fast and complete: Enabling complete neural network verification with rapid and massively parallel incomplete verifiers
|
https://scholar.google.com/scholar?cluster=7107853993141989483&hl=en&as_sdt=0,6
| 4
| 2,021
|
A Geometric Analysis of Deep Generative Image Models and Its Applications
| 58
|
iclr
| 4
| 1
|
2023-06-18 09:25:38.076000
|
https://github.com/Animadversio/GAN-Geometry
| 38
|
The geometry of deep generative image models and its applications
|
https://scholar.google.com/scholar?cluster=1386616180509191154&hl=en&as_sdt=0,5
| 3
| 2,021
|
Solving Compositional Reinforcement Learning Problems via Task Reduction
| 16
|
iclr
| 1
| 0
|
2023-06-18 09:25:38.279000
|
https://github.com/IrisLi17/self-imitation-via-reduction
| 14
|
Solving compositional reinforcement learning problems via task reduction
|
https://scholar.google.com/scholar?cluster=15628616147808752058&hl=en&as_sdt=0,23
| 1
| 2,021
|
Acting in Delayed Environments with Non-Stationary Markov Policies
| 12
|
iclr
| 5
| 0
|
2023-06-18 09:25:38.483000
|
https://github.com/galdl/rl_delay_basic
| 8
|
Acting in delayed environments with non-stationary markov policies
|
https://scholar.google.com/scholar?cluster=17360966560322895494&hl=en&as_sdt=0,33
| 1
| 2,021
|
Learnable Embedding sizes for Recommender Systems
| 44
|
iclr
| 9
| 0
|
2023-06-18 09:25:38.686000
|
https://github.com/ssui-liu/learnable-embed-sizes-for-RecSys
| 54
|
Learnable embedding sizes for recommender systems
|
https://scholar.google.com/scholar?cluster=803326739583596992&hl=en&as_sdt=0,33
| 2
| 2,021
|
Simple Spectral Graph Convolution
| 158
|
iclr
| 16
| 15
|
2023-06-18 09:25:38.890000
|
https://github.com/allenhaozhu/SSGC
| 70
|
Simple spectral graph convolution
|
https://scholar.google.com/scholar?cluster=3312425761995361615&hl=en&as_sdt=0,5
| 5
| 2,021
|
Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization
| 12
|
iclr
| 0
| 1
|
2023-06-18 09:43:24.233000
|
https://github.com/conditionWang/NTL
| 21
|
Non-transferable learning: A new approach for model ownership verification and applicability authorization
|
https://scholar.google.com/scholar?cluster=9579671006829239762&hl=en&as_sdt=0,31
| 2
| 2,022
|
Neural Structured Prediction for Inductive Node Classification
| 6
|
iclr
| 2
| 2
|
2023-06-18 09:43:24.437000
|
https://github.com/deepgraphlearning/spn
| 27
|
Neural structured prediction for inductive node classification
|
https://scholar.google.com/scholar?cluster=2079533968187968682&hl=en&as_sdt=0,5
| 4
| 2,022
|
Data-Efficient Graph Grammar Learning for Molecular Generation
| 16
|
iclr
| 21
| 4
|
2023-06-18 09:43:24.640000
|
https://github.com/gmh14/data_efficient_grammar
| 76
|
Data-efficient graph grammar learning for molecular generation
|
https://scholar.google.com/scholar?cluster=3349437997127524473&hl=en&as_sdt=0,5
| 2
| 2,022
|
Weighted Training for Cross-Task Learning
| 16
|
iclr
| 0
| 0
|
2023-06-18 09:43:24.843000
|
https://github.com/HornHehhf/TAWT
| 0
|
Weighted training for cross-task learning
|
https://scholar.google.com/scholar?cluster=5570518371918150850&hl=en&as_sdt=0,22
| 0
| 2,022
|
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling
| 21
|
iclr
| 16
| 7
|
2023-06-18 09:43:25.047000
|
https://github.com/magenta/midi-ddsp
| 265
|
MIDI-DDSP: Detailed control of musical performance via hierarchical modeling
|
https://scholar.google.com/scholar?cluster=13729627625392909520&hl=en&as_sdt=0,4
| 11
| 2,022
|
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting
| 93
|
iclr
| 31
| 23
|
2023-06-18 09:43:25.249000
|
https://github.com/alipay/Pyraformer
| 164
|
Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting
|
https://scholar.google.com/scholar?cluster=7428445395903169510&hl=en&as_sdt=0,5
| 3
| 2,022
|
StyleAlign: Analysis and Applications of Aligned StyleGAN Models
| 31
|
iclr
| 5
| 6
|
2023-06-18 09:43:25.452000
|
https://github.com/betterze/StyleAlign
| 145
|
Stylealign: Analysis and applications of aligned stylegan models
|
https://scholar.google.com/scholar?cluster=11079296793136133967&hl=en&as_sdt=0,44
| 18
| 2,022
|
Efficiently Modeling Long Sequences with Structured State Spaces
| 127
|
iclr
| 161
| 22
|
2023-06-18 09:43:25.655000
|
https://github.com/hazyresearch/state-spaces
| 1,219
|
Efficiently modeling long sequences with structured state spaces
|
https://scholar.google.com/scholar?cluster=8624959095392391416&hl=en&as_sdt=0,5
| 42
| 2,022
|
Large Language Models Can Be Strong Differentially Private Learners
| 105
|
iclr
| 17
| 4
|
2023-06-18 09:43:25.857000
|
https://github.com/lxuechen/private-transformers
| 101
|
Large language models can be strong differentially private learners
|
https://scholar.google.com/scholar?cluster=12835205672391916982&hl=en&as_sdt=0,5
| 5
| 2,022
|
GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation
| 142
|
iclr
| 47
| 8
|
2023-06-18 09:43:26.060000
|
https://github.com/minkaixu/geodiff
| 222
|
Geodiff: A geometric diffusion model for molecular conformation generation
|
https://scholar.google.com/scholar?cluster=4830391195637525286&hl=en&as_sdt=0,31
| 6
| 2,022
|
Learning Strides in Convolutional Neural Networks
| 22
|
iclr
| 6
| 2
|
2023-06-18 09:43:26.262000
|
https://github.com/google-research/diffstride
| 121
|
Learning strides in convolutional neural networks
|
https://scholar.google.com/scholar?cluster=1272651603956213806&hl=en&as_sdt=0,5
| 3
| 2,022
|
Understanding over-squashing and bottlenecks on graphs via curvature
| 136
|
iclr
| 7
| 2
|
2023-06-18 09:43:26.465000
|
https://github.com/jctops/understanding-oversquashing
| 30
|
Understanding over-squashing and bottlenecks on graphs via curvature
|
https://scholar.google.com/scholar?cluster=13989740203838615686&hl=en&as_sdt=0,33
| 3
| 2,022
|
Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme
| 23
|
iclr
| 93
| 15
|
2023-06-18 09:43:26.668000
|
https://github.com/huawei-noah/Speech-Backbones
| 396
|
Diffusion-based voice conversion with fast maximum likelihood sampling scheme
|
https://scholar.google.com/scholar?cluster=17487782166390673105&hl=en&as_sdt=0,36
| 26
| 2,022
|
Meta-Learning with Fewer Tasks through Task Interpolation
| 30
|
iclr
| 3
| 4
|
2023-06-18 09:43:26.871000
|
https://github.com/huaxiuyao/mlti
| 25
|
Meta-learning with fewer tasks through task interpolation
|
https://scholar.google.com/scholar?cluster=17468967265592568520&hl=en&as_sdt=0,5
| 3
| 2,022
|
Discovering and Explaining the Representation Bottleneck of DNNS
| 25
|
iclr
| 1
| 0
|
2023-06-18 09:43:27.074000
|
https://github.com/nebularaid2000/bottleneck
| 34
|
Discovering and explaining the representation bottleneck of dnns
|
https://scholar.google.com/scholar?cluster=6321522337570019810&hl=en&as_sdt=0,33
| 1
| 2,022
|
Sparse Communication via Mixed Distributions
| 5
|
iclr
| 1
| 0
|
2023-06-18 09:43:27.278000
|
https://github.com/deep-spin/sparse-communication
| 11
|
Sparse communication via mixed distributions
|
https://scholar.google.com/scholar?cluster=9090566515327405784&hl=en&as_sdt=0,31
| 5
| 2,022
|
Finetuned Language Models are Zero-Shot Learners
| 589
|
iclr
| 111
| 12
|
2023-06-18 09:43:27.481000
|
https://github.com/google-research/flan
| 966
|
Finetuned language models are zero-shot learners
|
https://scholar.google.com/scholar?cluster=3582238432300098245&hl=en&as_sdt=0,5
| 28
| 2,022
|
F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization
| 13
|
iclr
| 13
| 0
|
2023-06-18 09:43:27.684000
|
https://github.com/snap-research/f8net
| 89
|
F8net: Fixed-point 8-bit only multiplication for network quantization
|
https://scholar.google.com/scholar?cluster=9661231870650652462&hl=en&as_sdt=0,15
| 14
| 2,022
|
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design
| 12
|
iclr
| 10
| 1
|
2023-06-18 09:43:27.886000
|
https://github.com/Khrylx/Transform2Act
| 38
|
Transform2act: Learning a transform-and-control policy for efficient agent design
|
https://scholar.google.com/scholar?cluster=871690359216860608&hl=en&as_sdt=0,34
| 3
| 2,022
|
ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics
| 4
|
iclr
| 0
| 0
|
2023-06-18 09:43:28.089000
|
https://github.com/boreshkinai/protores
| 1
|
ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics
|
https://scholar.google.com/scholar?cluster=4035812674200440521&hl=en&as_sdt=0,5
| 1
| 2,022
|
CycleMLP: A MLP-like Architecture for Dense Prediction
| 130
|
iclr
| 26
| 2
|
2023-06-18 09:43:28.293000
|
https://github.com/ShoufaChen/CycleMLP
| 259
|
Cyclemlp: A mlp-like architecture for dense prediction
|
https://scholar.google.com/scholar?cluster=1322906163224921925&hl=en&as_sdt=0,23
| 3
| 2,022
|
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models
| 103
|
iclr
| 11
| 0
|
2023-06-18 09:43:28.496000
|
https://github.com/baofff/Analytic-DPM
| 138
|
Analytic-dpm: an analytic estimate of the optimal reverse variance in diffusion probabilistic models
|
https://scholar.google.com/scholar?cluster=799884416375929942&hl=en&as_sdt=0,5
| 2
| 2,022
|
The Information Geometry of Unsupervised Reinforcement Learning
| 17
|
iclr
| 3
| 1
|
2023-06-18 09:43:28.698000
|
https://github.com/ben-eysenbach/info_geometry
| 19
|
The information geometry of unsupervised reinforcement learning
|
https://scholar.google.com/scholar?cluster=1840572653029125797&hl=en&as_sdt=0,33
| 3
| 2,022
|
Language modeling via stochastic processes
| 15
|
iclr
| 11
| 4
|
2023-06-18 09:43:28.901000
|
https://github.com/rosewang2008/language_modeling_via_stochastic_processes
| 120
|
Language modeling via stochastic processes
|
https://scholar.google.com/scholar?cluster=15213113550965798696&hl=en&as_sdt=0,33
| 7
| 2,022
|
Learning to Downsample for Segmentation of Ultra-High Resolution Images
| 17
|
iclr
| 6
| 3
|
2023-06-18 09:43:29.106000
|
https://github.com/lxasqjc/Deformation-Segmentation
| 35
|
Learning to downsample for segmentation of ultra-high resolution images
|
https://scholar.google.com/scholar?cluster=11044772985924964414&hl=en&as_sdt=0,7
| 2
| 2,022
|
Variational Neural Cellular Automata
| 6
|
iclr
| 3
| 1
|
2023-06-18 09:43:29.308000
|
https://github.com/rasmusbergpalm/vnca
| 40
|
Variational neural cellular automata
|
https://scholar.google.com/scholar?cluster=8036499533836302391&hl=en&as_sdt=0,33
| 7
| 2,022
|
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?
| 4
|
iclr
| 3
| 0
|
2023-06-18 09:43:29.512000
|
https://github.com/shams-sam/fedoptim
| 10
|
Recycling model updates in federated learning: Are gradient subspaces low-rank?
|
https://scholar.google.com/scholar?cluster=11357128239739448107&hl=en&as_sdt=0,5
| 1
| 2,022
|
Sample and Computation Redistribution for Efficient Face Detection
| 49
|
iclr
| 4,436
| 910
|
2023-06-18 09:43:29.715000
|
https://github.com/deepinsight/insightface
| 16,066
|
Sample and computation redistribution for efficient face detection
|
https://scholar.google.com/scholar?cluster=249972322094479786&hl=en&as_sdt=0,50
| 479
| 2,022
|
Sound Adversarial Audio-Visual Navigation
| 12
|
iclr
| 0
| 1
|
2023-06-18 09:43:29.918000
|
https://github.com/yyf17/saavn
| 12
|
Sound adversarial audio-visual navigation
|
https://scholar.google.com/scholar?cluster=14696002671492155830&hl=en&as_sdt=0,3
| 2
| 2,022
|
Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations
| 16
|
iclr
| 1
| 0
|
2023-06-18 09:43:30.120000
|
https://github.com/rajesh-lab/nurd-code-public
| 6
|
Out-of-distribution generalization in the presence of nuisance-induced spurious correlations
|
https://scholar.google.com/scholar?cluster=11021328735736547096&hl=en&as_sdt=0,11
| 2
| 2,022
|
AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis
| 24
|
iclr
| 4
| 0
|
2023-06-18 09:43:30.323000
|
https://github.com/junfenggo/aeva-blackbox-backdoor-detection-main
| 22
|
Aeva: Black-box backdoor detection using adversarial extreme value analysis
|
https://scholar.google.com/scholar?cluster=1218468715415331882&hl=en&as_sdt=0,43
| 0
| 2,022
|
Top-label calibration and multiclass-to-binary reductions
| 17
|
iclr
| 5
| 0
|
2023-06-18 09:43:30.535000
|
https://github.com/aigen/df-posthoc-calibration
| 31
|
Top-label calibration and multiclass-to-binary reductions
|
https://scholar.google.com/scholar?cluster=5210721734640980720&hl=en&as_sdt=0,33
| 1
| 2,022
|
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future
| 9
|
iclr
| 2
| 0
|
2023-06-18 09:43:30.738000
|
https://github.com/AdityaLab/Back2Future
| 7
|
Back2future: Leveraging backfill dynamics for improving real-time predictions in future
|
https://scholar.google.com/scholar?cluster=4140733824788970279&hl=en&as_sdt=0,44
| 3
| 2,022
|
Approximation and Learning with Deep Convolutional Models: a Kernel Perspective
| 9
|
iclr
| 3
| 1
|
2023-06-18 09:43:30.940000
|
https://github.com/albietz/ckn_kernel
| 13
|
Approximation and learning with deep convolutional models: a kernel perspective
|
https://scholar.google.com/scholar?cluster=16497248736027137488&hl=en&as_sdt=0,5
| 2
| 2,022
|
CrossBeam: Learning to Search in Bottom-Up Program Synthesis
| 5
|
iclr
| 7
| 0
|
2023-06-18 09:43:31.143000
|
https://github.com/google-research/crossbeam
| 35
|
CrossBeam: Learning to search in bottom-up program synthesis
|
https://scholar.google.com/scholar?cluster=14342383468818615250&hl=en&as_sdt=0,5
| 7
| 2,022
|
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining
| 12
|
iclr
| 1
| 1
|
2023-06-18 09:43:31.355000
|
https://github.com/AhmedImtiazPrio/MaGNET
| 24
|
Magnet: Uniform sampling from deep generative network manifolds without retraining
|
https://scholar.google.com/scholar?cluster=18438387827991567060&hl=en&as_sdt=0,5
| 1
| 2,022
|
PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks
| 5
|
iclr
| 3
| 3
|
2023-06-18 09:43:31.558000
|
https://github.com/liusiyan/PI3NN
| 7
|
PI3NN: Out-of-distribution-aware prediction intervals from three neural networks
|
https://scholar.google.com/scholar?cluster=9729426911336956537&hl=en&as_sdt=0,5
| 3
| 2,022
|
It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation
| 6
|
iclr
| 0
| 0
|
2023-06-18 09:43:31.761000
|
https://github.com/yuqingd/cusp
| 11
|
It takes four to tango: Multiagent selfplay for automatic curriculum generation
|
https://scholar.google.com/scholar?cluster=12921508805700086972&hl=en&as_sdt=0,33
| 2
| 2,022
|
CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing
| 24
|
iclr
| 2
| 1
|
2023-06-18 09:43:31.965000
|
https://github.com/ai-secure/crop
| 5
|
Crop: Certifying robust policies for reinforcement learning through functional smoothing
|
https://scholar.google.com/scholar?cluster=15014236512905424649&hl=en&as_sdt=0,5
| 2
| 2,022
|
Neural Link Prediction with Walk Pooling
| 25
|
iclr
| 2
| 1
|
2023-06-18 09:43:32.169000
|
https://github.com/dadacheng/walkpooling
| 44
|
Neural link prediction with walk pooling
|
https://scholar.google.com/scholar?cluster=11799693892452603057&hl=en&as_sdt=0,5
| 3
| 2,022
|
Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators
| 10
|
iclr
| 1
| 3
|
2023-06-18 09:43:32.372000
|
https://github.com/microsoft/amos
| 23
|
Pretraining text encoders with adversarial mixture of training signal generators
|
https://scholar.google.com/scholar?cluster=9770552085778615131&hl=en&as_sdt=0,5
| 5
| 2,022
|
Non-Parallel Text Style Transfer with Self-Parallel Supervision
| 2
|
iclr
| 0
| 4
|
2023-06-18 09:43:32.577000
|
https://github.com/dapangliu/lamer
| 7
|
Non-Parallel Text Style Transfer with Self-Parallel Supervision
|
https://scholar.google.com/scholar?cluster=14757482519407793869&hl=en&as_sdt=0,33
| 2
| 2,022
|
Can an Image Classifier Suffice For Action Recognition?
| 8
|
iclr
| 8
| 3
|
2023-06-18 09:43:32.780000
|
https://github.com/ibm/sifar-pytorch
| 49
|
Can an image classifier suffice for action recognition?
|
https://scholar.google.com/scholar?cluster=13822718971656558432&hl=en&as_sdt=0,5
| 2
| 2,022
|
Interacting Contour Stochastic Gradient Langevin Dynamics
| 4
|
iclr
| 1
| 0
|
2023-06-18 09:43:32.983000
|
https://github.com/waynedw/interacting-contour-stochastic-gradient-langevin-dynamics
| 6
|
Interacting Contour Stochastic Gradient Langevin Dynamics
|
https://scholar.google.com/scholar?cluster=811536455190019406&hl=en&as_sdt=0,14
| 2
| 2,022
|
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?
| 25
|
iclr
| 5
| 0
|
2023-06-18 09:43:33.186000
|
https://github.com/rice-eic/patch-fool
| 20
|
Patch-fool: Are vision transformers always robust against adversarial perturbations?
|
https://scholar.google.com/scholar?cluster=1831846608432102028&hl=en&as_sdt=0,10
| 1
| 2,022
|
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
| 49
|
iclr
| 5
| 5
|
2023-06-18 09:43:33.388000
|
https://github.com/google-research/adamatch
| 54
|
Adamatch: A unified approach to semi-supervised learning and domain adaptation
|
https://scholar.google.com/scholar?cluster=9221339163655588943&hl=en&as_sdt=0,5
| 10
| 2,022
|
Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound
| 25
|
iclr
| 1
| 5
|
2023-06-18 09:43:33.591000
|
https://github.com/eth-sri/mn-bab
| 4
|
Complete verification via multi-neuron relaxation guided branch-and-bound
|
https://scholar.google.com/scholar?cluster=14769723255634252083&hl=en&as_sdt=0,41
| 5
| 2,022
|
Distribution Compression in Near-Linear Time
| 6
|
iclr
| 2
| 0
|
2023-06-18 09:43:33.796000
|
https://github.com/microsoft/goodpoints
| 31
|
Distribution compression in near-linear time
|
https://scholar.google.com/scholar?cluster=10525101075406225014&hl=en&as_sdt=0,5
| 9
| 2,022
|
Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networks
| 36
|
iclr
| 6
| 2
|
2023-06-18 09:43:33.999000
|
https://github.com/ZPdesu/MindTheGap
| 43
|
Mind the gap: Domain gap control for single shot domain adaptation for generative adversarial networks
|
https://scholar.google.com/scholar?cluster=12451250368858284023&hl=en&as_sdt=0,31
| 5
| 2,022
|
On Evaluation Metrics for Graph Generative Models
| 16
|
iclr
| 3
| 0
|
2023-06-18 09:43:34.208000
|
https://github.com/uoguelph-mlrg/ggm-metrics
| 16
|
On evaluation metrics for graph generative models
|
https://scholar.google.com/scholar?cluster=7803067086316285274&hl=en&as_sdt=0,33
| 2
| 2,022
|
Graph Condensation for Graph Neural Networks
| 43
|
iclr
| 10
| 2
|
2023-06-18 09:43:34.423000
|
https://github.com/chandlerbang/gcond
| 72
|
Graph condensation for graph neural networks
|
https://scholar.google.com/scholar?cluster=14491892748486687067&hl=en&as_sdt=0,5
| 4
| 2,022
|
Minimax Optimization with Smooth Algorithmic Adversaries
| 7
|
iclr
| 2
| 0
|
2023-06-18 09:43:34.628000
|
https://github.com/fiezt/minmax-opt-smooth-adversary
| 4
|
Minimax optimization with smooth algorithmic adversaries
|
https://scholar.google.com/scholar?cluster=11061521782135546152&hl=en&as_sdt=0,32
| 1
| 2,022
|
Leveraging unlabeled data to predict out-of-distribution performance
| 41
|
iclr
| 0
| 1
|
2023-06-18 09:43:34.831000
|
https://github.com/saurabhgarg1996/ATC_code
| 8
|
Leveraging unlabeled data to predict out-of-distribution performance
|
https://scholar.google.com/scholar?cluster=5646390275734787221&hl=en&as_sdt=0,39
| 1
| 2,022
|
VC dimension of partially quantized neural networks in the overparametrized regime
| 2
|
iclr
| 0
| 0
|
2023-06-18 09:43:35.034000
|
https://github.com/yutongwangumich/hann
| 1
|
Vc dimension of partially quantized neural networks in the overparametrized regime
|
https://scholar.google.com/scholar?cluster=11387455269961935968&hl=en&as_sdt=0,33
| 2
| 2,022
|
Optimal Representations for Covariate Shift
| 28
|
iclr
| 3
| 1
|
2023-06-18 09:43:35.237000
|
https://github.com/ryoungj/optdom
| 19
|
Optimal representations for covariate shift
|
https://scholar.google.com/scholar?cluster=2022985710361753356&hl=en&as_sdt=0,10
| 2
| 2,022
|
Fortuitous Forgetting in Connectionist Networks
| 11
|
iclr
| 4
| 1
|
2023-06-18 09:43:35.441000
|
https://github.com/hlml/fortuitous_forgetting
| 18
|
Fortuitous forgetting in connectionist networks
|
https://scholar.google.com/scholar?cluster=603488555859414419&hl=en&as_sdt=0,37
| 2
| 2,022
|
Contextualized Scene Imagination for Generative Commonsense Reasoning
| 11
|
iclr
| 2
| 1
|
2023-06-18 09:43:35.645000
|
https://github.com/wangpf3/imagine-and-verbalize
| 11
|
Contextualized scene imagination for generative commonsense reasoning
|
https://scholar.google.com/scholar?cluster=6593478295513742090&hl=en&as_sdt=0,27
| 1
| 2,022
|
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals
| 33
|
iclr
| 2
| 0
|
2023-06-18 09:43:35.848000
|
https://github.com/asmadotgh/dissect
| 11
|
Dissect: Disentangled simultaneous explanations via concept traversals
|
https://scholar.google.com/scholar?cluster=4475466485614086287&hl=en&as_sdt=0,33
| 2
| 2,022
|
Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series
| 5
|
iclr
| 10
| 3
|
2023-06-18 09:43:36.051000
|
https://github.com/reml-lab/hetvae
| 24
|
Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series
|
https://scholar.google.com/scholar?cluster=3281039634260173349&hl=en&as_sdt=0,5
| 3
| 2,022
|
Bayesian Framework for Gradient Leakage
| 16
|
iclr
| 3
| 1
|
2023-06-18 09:43:36.254000
|
https://github.com/eth-sri/bayes-framework-leakage
| 8
|
Bayesian framework for gradient leakage
|
https://scholar.google.com/scholar?cluster=14925580502725272742&hl=en&as_sdt=0,43
| 6
| 2,022
|
Maximum n-times Coverage for Vaccine Design
| 4
|
iclr
| 7
| 1
|
2023-06-18 09:43:36.458000
|
https://github.com/gifford-lab/optivax
| 22
|
Maximum n-times coverage for vaccine design
|
https://scholar.google.com/scholar?cluster=17184876342921372695&hl=en&as_sdt=0,5
| 16
| 2,022
|
KL Guided Domain Adaptation
| 14
|
iclr
| 2
| 0
|
2023-06-18 09:43:36.661000
|
https://github.com/atuannguyen/kl
| 6
|
KL guided domain adaptation
|
https://scholar.google.com/scholar?cluster=17961201142994065292&hl=en&as_sdt=0,5
| 2
| 2,022
|
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness
| 42
|
iclr
| 9
| 1
|
2023-06-18 09:43:36.864000
|
https://github.com/gnnaskernel/gnnaskernel
| 51
|
From stars to subgraphs: Uplifting any GNN with local structure awareness
|
https://scholar.google.com/scholar?cluster=4598272290624376922&hl=en&as_sdt=0,5
| 3
| 2,022
|
Gradient Importance Learning for Incomplete Observations
| 6
|
iclr
| 2
| 0
|
2023-06-18 09:43:37.067000
|
https://github.com/gaoqitong/gradient-importance-learning
| 1
|
Gradient importance learning for incomplete observations
|
https://scholar.google.com/scholar?cluster=3408438792226712835&hl=en&as_sdt=0,33
| 2
| 2,022
|
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset
| 8
|
iclr
| 0
| 0
|
2023-06-18 09:43:37.271000
|
https://github.com/berleon/do_users_benefit_from_interpretable_vision
| 4
|
Do users benefit from interpretable vision? a user study, baseline, and dataset
|
https://scholar.google.com/scholar?cluster=17643359548454161307&hl=en&as_sdt=0,5
| 3
| 2,022
|
Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL
| 25
|
iclr
| 3
| 1
|
2023-06-18 09:43:37.475000
|
https://github.com/umd-huang-lab/paad_adv_rl
| 3
|
Who is the strongest enemy? towards optimal and efficient evasion attacks in deep rl
|
https://scholar.google.com/scholar?cluster=16507433832957753266&hl=en&as_sdt=0,5
| 2
| 2,022
|
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