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Accelerating Reinforcement Learning through GPU Atari Emulation
| 18 |
neurips
| 32 | 15 |
2023-06-16 15:12:10.222000
|
https://github.com/NVLABs/cule
| 216 |
Accelerating reinforcement learning through gpu atari emulation
|
https://scholar.google.com/scholar?cluster=14852827801833804671&hl=en&as_sdt=0,5
| 20 | 2,020 |
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs
| 43 |
neurips
| 5 | 2 |
2023-06-16 15:12:10.414000
|
https://github.com/dtak/mbrl-smdp-ode
| 27 |
Model-based reinforcement learning for semi-markov decision processes with neural odes
|
https://scholar.google.com/scholar?cluster=6882030783154485592&hl=en&as_sdt=0,5
| 3 | 2,020 |
Graph Stochastic Neural Networks for Semi-supervised Learning
| 28 |
neurips
| 6 | 1 |
2023-06-16 15:12:10.607000
|
https://github.com/GSNN/GSNN
| 17 |
Graph stochastic neural networks for semi-supervised learning
|
https://scholar.google.com/scholar?cluster=12398431409964717174&hl=en&as_sdt=0,26
| 2 | 2,020 |
Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning
| 23 |
neurips
| 113 | 0 |
2023-06-16 15:12:10.799000
|
https://github.com/Thinklab-SJTU/ThinkMatch
| 714 |
Graduated assignment for joint multi-graph matching and clustering with application to unsupervised graph matching network learning
|
https://scholar.google.com/scholar?cluster=15043532701197211063&hl=en&as_sdt=0,43
| 22 | 2,020 |
Estimating Training Data Influence by Tracing Gradient Descent
| 145 |
neurips
| 14 | 4 |
2023-06-16 15:12:10.992000
|
https://github.com/frederick0329/TracIn
| 186 |
Estimating training data influence by tracing gradient descent
|
https://scholar.google.com/scholar?cluster=1975203419691170892&hl=en&as_sdt=0,5
| 8 | 2,020 |
Joint Policy Search for Multi-agent Collaboration with Imperfect Information
| 11 |
neurips
| 9 | 0 |
2023-06-16 15:12:11.187000
|
https://github.com/facebookresearch/jps
| 41 |
Joint policy search for multi-agent collaboration with imperfect information
|
https://scholar.google.com/scholar?cluster=9814706809980127110&hl=en&as_sdt=0,5
| 6 | 2,020 |
Learning Retrospective Knowledge with Reverse Reinforcement Learning
| 11 |
neurips
| 658 | 6 |
2023-06-16 15:12:11.380000
|
https://github.com/ShangtongZhang/DeepRL
| 2,943 |
Learning retrospective knowledge with reverse reinforcement learning
|
https://scholar.google.com/scholar?cluster=5697894321582614972&hl=en&as_sdt=0,34
| 93 | 2,020 |
Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data
| 8 |
neurips
| 1 | 2 |
2023-06-16 15:12:11.573000
|
https://github.com/mcogswell/dialog_without_dialog
| 5 |
Dialog without dialog data: Learning visual dialog agents from VQA data
|
https://scholar.google.com/scholar?cluster=15836872788471162855&hl=en&as_sdt=0,33
| 2 | 2,020 |
The Complete Lasso Tradeoff Diagram
| 7 |
neurips
| 0 | 0 |
2023-06-16 15:12:11.767000
|
https://github.com/HuaWang-wharton/CompleteLassoDiagram
| 0 |
The complete Lasso tradeoff diagram
|
https://scholar.google.com/scholar?cluster=11441396913332504259&hl=en&as_sdt=0,5
| 0 | 2,020 |
The Primal-Dual method for Learning Augmented Algorithms
| 77 |
neurips
| 0 | 0 |
2023-06-16 15:12:11.959000
|
https://github.com/etienne4/PDLA
| 3 |
The primal-dual method for learning augmented algorithms
|
https://scholar.google.com/scholar?cluster=17410161354545999384&hl=en&as_sdt=0,5
| 1 | 2,020 |
A Class of Algorithms for General Instrumental Variable Models
| 26 |
neurips
| 1 | 0 |
2023-06-16 15:12:12.156000
|
https://github.com/nikikilbertus/general-iv-models
| 13 |
A class of algorithms for general instrumental variable models
|
https://scholar.google.com/scholar?cluster=6114438229492187489&hl=en&as_sdt=0,5
| 3 | 2,020 |
Black-Box Ripper: Copying black-box models using generative evolutionary algorithms
| 25 |
neurips
| 3 | 3 |
2023-06-16 15:12:12.354000
|
https://github.com/antoniobarbalau/black-box-ripper
| 26 |
Black-Box Ripper: Copying black-box models using generative evolutionary algorithms
|
https://scholar.google.com/scholar?cluster=2038937056151338541&hl=en&as_sdt=0,5
| 2 | 2,020 |
Bayesian Optimization of Risk Measures
| 34 |
neurips
| 3 | 0 |
2023-06-16 15:12:12.547000
|
https://github.com/saitcakmak/BoRisk
| 20 |
Bayesian optimization of risk measures
|
https://scholar.google.com/scholar?cluster=11597649173870382888&hl=en&as_sdt=0,10
| 4 | 2,020 |
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search
| 24 |
neurips
| 3 | 0 |
2023-06-16 15:12:12.743000
|
https://github.com/tarungog/torsionnet_paper_version
| 12 |
Torsionnet: A reinforcement learning approach to sequential conformer search
|
https://scholar.google.com/scholar?cluster=15323026786978211130&hl=en&as_sdt=0,11
| 5 | 2,020 |
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
| 490 |
neurips
| 69 | 8 |
2023-06-16 15:12:12.940000
|
https://github.com/autonomousvision/graf
| 376 |
Graf: Generative radiance fields for 3d-aware image synthesis
|
https://scholar.google.com/scholar?cluster=6074305542312504170&hl=en&as_sdt=0,5
| 19 | 2,020 |
A Simple Language Model for Task-Oriented Dialogue
| 341 |
neurips
| 76 | 24 |
2023-06-16 15:12:13.133000
|
https://github.com/salesforce/simpletod
| 217 |
A simple language model for task-oriented dialogue
|
https://scholar.google.com/scholar?cluster=13901694758455015611&hl=en&as_sdt=0,43
| 13 | 2,020 |
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval
| 10 |
neurips
| 0 | 0 |
2023-06-16 15:12:13.325000
|
https://github.com/fawuuu/mirror_spr
| 0 |
A continuous-time mirror descent approach to sparse phase retrieval
|
https://scholar.google.com/scholar?cluster=17231492085001366085&hl=en&as_sdt=0,5
| 1 | 2,020 |
Confidence sequences for sampling without replacement
| 16 |
neurips
| 0 | 0 |
2023-06-16 15:12:13.536000
|
https://github.com/wannabesmith/confseq_wor
| 4 |
Confidence sequences for sampling without replacement
|
https://scholar.google.com/scholar?cluster=4371792767519028336&hl=en&as_sdt=0,33
| 2 | 2,020 |
Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge
| 75 |
neurips
| 1 | 1 |
2023-06-16 15:12:13.728000
|
https://github.com/alontalmor/TeachYourAI
| 44 |
Leap-of-thought: Teaching pre-trained models to systematically reason over implicit knowledge
|
https://scholar.google.com/scholar?cluster=11221279526378822822&hl=en&as_sdt=0,33
| 6 | 2,020 |
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games
| 49 |
neurips
| 16 | 2 |
2023-06-16 15:12:13.922000
|
https://github.com/JBLanier/pipeline-psro
| 37 |
Pipeline psro: A scalable approach for finding approximate nash equilibria in large games
|
https://scholar.google.com/scholar?cluster=8078944900964563231&hl=en&as_sdt=0,5
| 4 | 2,020 |
Latent Template Induction with Gumbel-CRFs
| 8 |
neurips
| 8 | 0 |
2023-06-16 15:12:14.115000
|
https://github.com/FranxYao/Gumbel-CRF
| 53 |
Latent template induction with Gumbel-CRFS
|
https://scholar.google.com/scholar?cluster=11572320243625839339&hl=en&as_sdt=0,45
| 5 | 2,020 |
Factorizable Graph Convolutional Networks
| 110 |
neurips
| 9 | 1 |
2023-06-16 15:12:14.307000
|
https://github.com/ihollywhy/FactorGCN.PyTorch
| 47 |
Factorizable graph convolutional networks
|
https://scholar.google.com/scholar?cluster=8785212060536911333&hl=en&as_sdt=0,5
| 1 | 2,020 |
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
| 54 |
neurips
| 8 | 0 |
2023-06-16 15:12:14.500000
|
https://github.com/val-iisc/GAMA-GAT
| 23 |
Guided adversarial attack for evaluating and enhancing adversarial defenses
|
https://scholar.google.com/scholar?cluster=8789193805515156711&hl=en&as_sdt=0,4
| 13 | 2,020 |
A Study on Encodings for Neural Architecture Search
| 62 |
neurips
| 5 | 0 |
2023-06-16 15:12:14.692000
|
https://github.com/naszilla/nas-encodings
| 29 |
A study on encodings for neural architecture search
|
https://scholar.google.com/scholar?cluster=10654503174667687184&hl=en&as_sdt=0,5
| 8 | 2,020 |
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
| 56 |
neurips
| 14 | 1 |
2023-06-16 15:12:14.885000
|
https://github.com/divelab/Noise2Same
| 61 |
Noise2Same: Optimizing a self-supervised bound for image denoising
|
https://scholar.google.com/scholar?cluster=11034449771862821776&hl=en&as_sdt=0,47
| 4 | 2,020 |
Early-Learning Regularization Prevents Memorization of Noisy Labels
| 304 |
neurips
| 28 | 5 |
2023-06-16 15:12:15.078000
|
https://github.com/shengliu66/ELR
| 249 |
Early-learning regularization prevents memorization of noisy labels
|
https://scholar.google.com/scholar?cluster=3805522034549943304&hl=en&as_sdt=0,25
| 8 | 2,020 |
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond
| 71 |
neurips
| 33 | 11 |
2023-06-16 15:12:15.271000
|
https://github.com/dvlab-research/Simple-SR
| 215 |
Lapar: Linearly-assembled pixel-adaptive regression network for single image super-resolution and beyond
|
https://scholar.google.com/scholar?cluster=5145084170737435928&hl=en&as_sdt=0,6
| 5 | 2,020 |
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot
| 48 |
neurips
| 5 | 1 |
2023-06-16 15:12:15.464000
|
https://github.com/JingtongSu/sanity-checking-pruning
| 39 |
Sanity-checking pruning methods: Random tickets can win the jackpot
|
https://scholar.google.com/scholar?cluster=2172629804299709441&hl=en&as_sdt=0,33
| 2 | 2,020 |
Position-based Scaled Gradient for Model Quantization and Pruning
| 24 |
neurips
| 3 | 1 |
2023-06-16 15:12:15.658000
|
https://github.com/Jangho-Kim/PSG-pytorch
| 17 |
Position-based scaled gradient for model quantization and pruning
|
https://scholar.google.com/scholar?cluster=1487663288303677561&hl=en&as_sdt=0,5
| 3 | 2,020 |
Graph Information Bottleneck
| 105 |
neurips
| 25 | 1 |
2023-06-16 15:12:15.853000
|
https://github.com/snap-stanford/GIB
| 104 |
Graph information bottleneck
|
https://scholar.google.com/scholar?cluster=11004655296553092045&hl=en&as_sdt=0,5
| 44 | 2,020 |
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
| 39 |
neurips
| 369 | 28 |
2023-06-16 15:12:16.052000
|
https://github.com/Microsoft/EdgeML
| 1,453 |
RNNPool: Efficient non-linear pooling for RAM constrained inference
|
https://scholar.google.com/scholar?cluster=9340951550254223370&hl=en&as_sdt=0,5
| 87 | 2,020 |
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation
| 22 |
neurips
| 2 | 1 |
2023-06-16 15:12:16.246000
|
https://github.com/Jacobew/AutoPanoptic
| 19 |
Auto-panoptic: Cooperative multi-component architecture search for panoptic segmentation
|
https://scholar.google.com/scholar?cluster=11947807024654626869&hl=en&as_sdt=0,6
| 2 | 2,020 |
On Completeness-aware Concept-Based Explanations in Deep Neural Networks
| 162 |
neurips
| 13 | 1 |
2023-06-16 15:12:16.439000
|
https://github.com/chihkuanyeh/concept_exp
| 42 |
On completeness-aware concept-based explanations in deep neural networks
|
https://scholar.google.com/scholar?cluster=1524554551065921155&hl=en&as_sdt=0,5
| 4 | 2,020 |
Why Normalizing Flows Fail to Detect Out-of-Distribution Data
| 147 |
neurips
| 11 | 9 |
2023-06-16 15:12:16.632000
|
https://github.com/PolinaKirichenko/flows_ood
| 79 |
Why normalizing flows fail to detect out-of-distribution data
|
https://scholar.google.com/scholar?cluster=2771286037773844242&hl=en&as_sdt=0,47
| 2 | 2,020 |
Unsupervised Translation of Programming Languages
| 105 |
neurips
| 114 | 33 |
2023-06-16 15:12:16.825000
|
https://github.com/facebookresearch/CodeGen
| 540 |
Unsupervised translation of programming languages
|
https://scholar.google.com/scholar?cluster=1104657131784756679&hl=en&as_sdt=0,21
| 31 | 2,020 |
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
| 76 |
neurips
| 12 | 1 |
2023-06-16 15:12:17.018000
|
https://github.com/RoyalVane/ASM
| 69 |
Adversarial style mining for one-shot unsupervised domain adaptation
|
https://scholar.google.com/scholar?cluster=12682829350097829096&hl=en&as_sdt=0,5
| 5 | 2,020 |
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder
| 120 |
neurips
| 6 | 2 |
2023-06-16 15:12:17.211000
|
https://github.com/XavierXiao/Likelihood-Regret
| 36 |
Likelihood regret: An out-of-distribution detection score for variational auto-encoder
|
https://scholar.google.com/scholar?cluster=17961908496712601770&hl=en&as_sdt=0,5
| 4 | 2,020 |
Meta-Learning through Hebbian Plasticity in Random Networks
| 61 |
neurips
| 20 | 0 |
2023-06-16 15:12:17.403000
|
https://github.com/enajx/HebbianMetaLearning
| 103 |
Meta-learning through hebbian plasticity in random networks
|
https://scholar.google.com/scholar?cluster=14182623640516258528&hl=en&as_sdt=0,47
| 1 | 2,020 |
Statistical and Topological Properties of Sliced Probability Divergences
| 44 |
neurips
| 2 | 0 |
2023-06-16 15:12:17.596000
|
https://github.com/kimiandj/sliced_div
| 0 |
Statistical and topological properties of sliced probability divergences
|
https://scholar.google.com/scholar?cluster=12747887556426720635&hl=en&as_sdt=0,5
| 1 | 2,020 |
Probabilistic Active Meta-Learning
| 28 |
neurips
| 5 | 0 |
2023-06-16 15:12:17.791000
|
https://github.com/jeankaddour/paml
| 15 |
Probabilistic active meta-learning
|
https://scholar.google.com/scholar?cluster=10986627198228240905&hl=en&as_sdt=0,5
| 1 | 2,020 |
Linearly Converging Error Compensated SGD
| 58 |
neurips
| 0 | 0 |
2023-06-16 15:12:17.986000
|
https://github.com/eduardgorbunov/ef_sigma_k
| 1 |
Linearly converging error compensated SGD
|
https://scholar.google.com/scholar?cluster=9254067822190880000&hl=en&as_sdt=0,5
| 1 | 2,020 |
Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction
| 8 |
neurips
| 3 | 2 |
2023-06-16 15:12:18.178000
|
https://github.com/facebookresearch/c3dm
| 18 |
Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction
|
https://scholar.google.com/scholar?cluster=11150205973301180781&hl=en&as_sdt=0,5
| 11 | 2,020 |
The Cone of Silence: Speech Separation by Localization
| 35 |
neurips
| 20 | 3 |
2023-06-16 15:12:18.371000
|
https://github.com/vivjay30/Cone-of-Silence
| 127 |
The cone of silence: Speech separation by localization
|
https://scholar.google.com/scholar?cluster=8905558076062704423&hl=en&as_sdt=0,33
| 11 | 2,020 |
High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds
| 12 |
neurips
| 4 | 0 |
2023-06-16 15:12:18.564000
|
https://github.com/NoemieJaquier/GaBOtorch
| 38 |
High-dimensional Bayesian optimization via nested Riemannian manifolds
|
https://scholar.google.com/scholar?cluster=1646248372513417394&hl=en&as_sdt=0,5
| 2 | 2,020 |
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula
| 17 |
neurips
| 0 | 0 |
2023-06-16 15:12:18.758000
|
https://github.com/yuxuanzhao2295/Matrix-Completion-with-Quantified-Uncertainty-through-Low-Rank-Gaussian-Copula
| 1 |
Matrix completion with quantified uncertainty through low rank gaussian copula
|
https://scholar.google.com/scholar?cluster=18308777915678894427&hl=en&as_sdt=0,5
| 1 | 2,020 |
Sparse and Continuous Attention Mechanisms
| 20 |
neurips
| 2 | 0 |
2023-06-16 15:12:18.950000
|
https://github.com/deep-spin/mcan-vqa-continuous-attention
| 20 |
Sparse and continuous attention mechanisms
|
https://scholar.google.com/scholar?cluster=8098274056344502290&hl=en&as_sdt=0,5
| 4 | 2,020 |
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
| 415 |
neurips
| 70 | 25 |
2023-06-16 15:12:19.143000
|
https://github.com/implus/GFocal
| 546 |
Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection
|
https://scholar.google.com/scholar?cluster=16305632232773240100&hl=en&as_sdt=0,18
| 14 | 2,020 |
Learning by Minimizing the Sum of Ranked Range
| 12 |
neurips
| 1 | 0 |
2023-06-16 15:12:19.336000
|
https://github.com/discovershu/SoRR
| 10 |
Learning by minimizing the sum of ranked range
|
https://scholar.google.com/scholar?cluster=5995735188540741359&hl=en&as_sdt=0,44
| 1 | 2,020 |
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations
| 151 |
neurips
| 16 | 3 |
2023-06-16 15:12:19.570000
|
https://github.com/chenhongge/StateAdvDRL
| 90 |
Robust deep reinforcement learning against adversarial perturbations on state observations
|
https://scholar.google.com/scholar?cluster=4468368848724952344&hl=en&as_sdt=0,23
| 5 | 2,020 |
Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features
| 61 |
neurips
| 3 | 1 |
2023-06-16 15:12:19.772000
|
https://github.com/boschresearch/hierarchical_anomaly_detection
| 40 |
Understanding anomaly detection with deep invertible networks through hierarchies of distributions and features
|
https://scholar.google.com/scholar?cluster=16029376900610177245&hl=en&as_sdt=0,39
| 5 | 2,020 |
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
| 10 |
neurips
| 0 | 0 |
2023-06-16 15:12:19.966000
|
https://github.com/MInner/lrmf
| 1 |
Log-likelihood ratio minimizing flows: Towards robust and quantifiable neural distribution alignment
|
https://scholar.google.com/scholar?cluster=13503385515572497823&hl=en&as_sdt=0,34
| 1 | 2,020 |
Implicit Regularization in Deep Learning May Not Be Explainable by Norms
| 108 |
neurips
| 2 | 1 |
2023-06-16 15:12:20.159000
|
https://github.com/noamrazin/imp_reg_dl_not_norms
| 7 |
Implicit regularization in deep learning may not be explainable by norms
|
https://scholar.google.com/scholar?cluster=15094324317237150803&hl=en&as_sdt=0,44
| 3 | 2,020 |
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
| 97 |
neurips
| 24 | 0 |
2023-06-16 15:12:20.352000
|
https://github.com/yd-kwon/POMO
| 96 |
Pomo: Policy optimization with multiple optima for reinforcement learning
|
https://scholar.google.com/scholar?cluster=10640697018374796647&hl=en&as_sdt=0,5
| 2 | 2,020 |
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
| 21 |
neurips
| 6 | 0 |
2023-06-16 15:12:20.550000
|
https://github.com/ispc-lab/RSKDD-Net
| 34 |
Rskdd-net: Random sample-based keypoint detector and descriptor
|
https://scholar.google.com/scholar?cluster=4142945676817836667&hl=en&as_sdt=0,33
| 2 | 2,020 |
ContraGAN: Contrastive Learning for Conditional Image Generation
| 105 |
neurips
| 316 | 30 |
2023-06-16 15:12:20.744000
|
https://github.com/POSTECH-CVLab/PyTorch-StudioGAN
| 3,190 |
Contragan: Contrastive learning for conditional image generation
|
https://scholar.google.com/scholar?cluster=18317588262394095158&hl=en&as_sdt=0,44
| 52 | 2,020 |
On the distance between two neural networks and the stability of learning
| 38 |
neurips
| 7 | 0 |
2023-06-16 15:12:20.937000
|
https://github.com/jxbz/fromage
| 116 |
On the distance between two neural networks and the stability of learning
|
https://scholar.google.com/scholar?cluster=16791561363789203322&hl=en&as_sdt=0,5
| 6 | 2,020 |
A Topological Filter for Learning with Label Noise
| 58 |
neurips
| 7 | 2 |
2023-06-16 15:12:21.129000
|
https://github.com/pxiangwu/TopoFilter
| 21 |
A topological filter for learning with label noise
|
https://scholar.google.com/scholar?cluster=3115391967239595458&hl=en&as_sdt=0,47
| 3 | 2,020 |
Personalized Federated Learning with Moreau Envelopes
| 454 |
neurips
| 81 | 3 |
2023-06-16 15:12:21.321000
|
https://github.com/CharlieDinh/pFedMe
| 243 |
Personalized federated learning with moreau envelopes
|
https://scholar.google.com/scholar?cluster=17442117675158664178&hl=en&as_sdt=0,5
| 3 | 2,020 |
Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters
| 11 |
neurips
| 3 | 0 |
2023-06-16 15:12:21.546000
|
https://github.com/PrincetonLIPS/AHGP
| 20 |
Task-agnostic amortized inference of gaussian process hyperparameters
|
https://scholar.google.com/scholar?cluster=12673972723308026781&hl=en&as_sdt=0,5
| 4 | 2,020 |
Energy-based Out-of-distribution Detection
| 527 |
neurips
| 52 | 0 |
2023-06-16 15:12:21.740000
|
https://github.com/wetliu/energy_ood
| 326 |
Energy-based out-of-distribution detection
|
https://scholar.google.com/scholar?cluster=6749168752375875068&hl=en&as_sdt=0,14
| 8 | 2,020 |
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
| 52 |
neurips
| 4 | 0 |
2023-06-16 15:12:21.932000
|
https://github.com/liuchen11/AdversaryLossLandscape
| 32 |
On the loss landscape of adversarial training: Identifying challenges and how to overcome them
|
https://scholar.google.com/scholar?cluster=5092768704180341925&hl=en&as_sdt=0,50
| 2 | 2,020 |
User-Dependent Neural Sequence Models for Continuous-Time Event Data
| 10 |
neurips
| 5 | 0 |
2023-06-16 15:12:22.126000
|
https://github.com/ajboyd2/vae_mpp
| 10 |
User-dependent neural sequence models for continuous-time event data
|
https://scholar.google.com/scholar?cluster=18086639497022008062&hl=en&as_sdt=0,36
| 1 | 2,020 |
Active Structure Learning of Causal DAGs via Directed Clique Trees
| 23 |
neurips
| 1 | 0 |
2023-06-16 15:12:22.320000
|
https://github.com/csquires/dct-policy
| 5 |
Active structure learning of causal DAGs via directed clique trees
|
https://scholar.google.com/scholar?cluster=2190615991629114246&hl=en&as_sdt=0,5
| 3 | 2,020 |
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
| 51 |
neurips
| 1 | 0 |
2023-06-16 15:12:22.559000
|
https://github.com/nkeriven/random-graph-gnn
| 12 |
Convergence and stability of graph convolutional networks on large random graphs
|
https://scholar.google.com/scholar?cluster=8332036655143866488&hl=en&as_sdt=0,33
| 1 | 2,020 |
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
| 408 |
neurips
| 319 | 64 |
2023-06-16 15:12:22.752000
|
https://github.com/pytorch/botorch
| 2,663 |
BoTorch: A framework for efficient Monte-Carlo Bayesian optimization
|
https://scholar.google.com/scholar?cluster=1764580662257780594&hl=en&as_sdt=0,5
| 51 | 2,020 |
Reconsidering Generative Objectives For Counterfactual Reasoning
| 19 |
neurips
| 3 | 0 |
2023-06-16 15:12:22.945000
|
https://github.com/DannieLu/BV-NICE
| 10 |
Reconsidering generative objectives for counterfactual reasoning
|
https://scholar.google.com/scholar?cluster=17354375508713844403&hl=en&as_sdt=0,14
| 3 | 2,020 |
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
| 2 |
neurips
| 1 | 4 |
2023-06-16 15:12:23.138000
|
https://github.com/RuiZhang2016/Quantile-Propagation-for-Wasserstein-Approximate-Gaussian-Processes
| 0 |
Quantile propagation for wasserstein-approximate gaussian processes
|
https://scholar.google.com/scholar?cluster=12042120379690917891&hl=en&as_sdt=0,36
| 2 | 2,020 |
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning
| 48 |
neurips
| 6 | 1 |
2023-06-16 15:12:23.368000
|
https://github.com/trzhang0116/HRAC
| 29 |
Generating adjacency-constrained subgoals in hierarchical reinforcement learning
|
https://scholar.google.com/scholar?cluster=16065617713704817902&hl=en&as_sdt=0,5
| 2 | 2,020 |
High-contrast “gaudy” images improve the training of deep neural network models of visual cortex
| 3 |
neurips
| 1 | 0 |
2023-06-16 15:12:23.592000
|
https://github.com/pillowlab/gaudy-images
| 5 |
High-contrast “gaudy” images improve the training of deep neural network models of visual cortex
|
https://scholar.google.com/scholar?cluster=3979615738480690604&hl=en&as_sdt=0,11
| 8 | 2,020 |
Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion
| 46 |
neurips
| 7 | 0 |
2023-06-16 15:12:23.785000
|
https://github.com/MIRALab-USTC/KGE-DURA
| 42 |
Duality-induced regularizer for tensor factorization based knowledge graph completion
|
https://scholar.google.com/scholar?cluster=2035583007156508987&hl=en&as_sdt=0,10
| 3 | 2,020 |
H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks
| 9 |
neurips
| 3 | 0 |
2023-06-16 15:12:23.980000
|
https://github.com/IGITUGraz/H-Mem
| 9 |
H-mem: Harnessing synaptic plasticity with hebbian memory networks
|
https://scholar.google.com/scholar?cluster=16583441948206163184&hl=en&as_sdt=0,47
| 5 | 2,020 |
Curriculum By Smoothing
| 42 |
neurips
| 4 | 1 |
2023-06-16 15:12:24.174000
|
https://github.com/pairlab/CBS
| 38 |
Curriculum by smoothing
|
https://scholar.google.com/scholar?cluster=13722465389000493780&hl=en&as_sdt=0,33
| 4 | 2,020 |
Fast Transformers with Clustered Attention
| 95 |
neurips
| 161 | 28 |
2023-06-16 15:12:24.367000
|
https://github.com/idiap/fast-transformers
| 1,433 |
Fast transformers with clustered attention
|
https://scholar.google.com/scholar?cluster=12028542204791594532&hl=en&as_sdt=0,1
| 27 | 2,020 |
Strongly Incremental Constituency Parsing with Graph Neural Networks
| 20 |
neurips
| 6 | 0 |
2023-06-16 15:12:24.562000
|
https://github.com/princeton-vl/attach-juxtapose-parser
| 30 |
Strongly incremental constituency parsing with graph neural networks
|
https://scholar.google.com/scholar?cluster=11445099204030608115&hl=en&as_sdt=0,22
| 5 | 2,020 |
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians
| 19 |
neurips
| 9 | 0 |
2023-06-16 15:12:24.764000
|
https://github.com/pomonam/Self-Tuning-Networks
| 46 |
Delta-stn: Efficient bilevel optimization for neural networks using structured response jacobians
|
https://scholar.google.com/scholar?cluster=4174355255756713694&hl=en&as_sdt=0,11
| 2 | 2,020 |
Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis
| 45 |
neurips
| 11 | 1 |
2023-06-16 15:12:24.957000
|
https://github.com/Khrylx/RFC
| 125 |
Residual force control for agile human behavior imitation and extended motion synthesis
|
https://scholar.google.com/scholar?cluster=14507242578909021405&hl=en&as_sdt=0,5
| 9 | 2,020 |
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
| 99 |
neurips
| 4 | 0 |
2023-06-16 15:12:25.151000
|
https://github.com/chrsmrrs/sparsewl
| 18 |
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
|
https://scholar.google.com/scholar?cluster=6518483326763541093&hl=en&as_sdt=0,47
| 1 | 2,020 |
Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion
| 16 |
neurips
| 2 | 0 |
2023-06-16 15:12:25.343000
|
https://github.com/maqqbu/MMSR
| 7 |
Adversarial crowdsourcing through robust rank-one matrix completion
|
https://scholar.google.com/scholar?cluster=13042202879705481913&hl=en&as_sdt=0,5
| 1 | 2,020 |
Learning Semantic-aware Normalization for Generative Adversarial Networks
| 8 |
neurips
| 3 | 1 |
2023-06-16 15:12:25.547000
|
https://github.com/researchmm/SariGAN
| 50 |
Learning semantic-aware normalization for generative adversarial networks
|
https://scholar.google.com/scholar?cluster=9760501643800019907&hl=en&as_sdt=0,11
| 19 | 2,020 |
Differentiable Causal Discovery from Interventional Data
| 88 |
neurips
| 9 | 0 |
2023-06-16 15:12:25.740000
|
https://github.com/slachapelle/dcdi
| 52 |
Differentiable causal discovery from interventional data
|
https://scholar.google.com/scholar?cluster=3426161106232828380&hl=en&as_sdt=0,23
| 4 | 2,020 |
Robust Persistence Diagrams using Reproducing Kernels
| 3 |
neurips
| 0 | 0 |
2023-06-16 15:12:25.934000
|
https://github.com/sidv23/robust-PDs
| 4 |
Robust persistence diagrams using reproducing kernels
|
https://scholar.google.com/scholar?cluster=18368713409545563505&hl=en&as_sdt=0,47
| 3 | 2,020 |
CrossTransformers: spatially-aware few-shot transfer
| 216 |
neurips
| 136 | 44 |
2023-06-16 15:12:26.127000
|
https://github.com/google-research/meta-dataset
| 698 |
Crosstransformers: spatially-aware few-shot transfer
|
https://scholar.google.com/scholar?cluster=17678351520585842037&hl=en&as_sdt=0,5
| 24 | 2,020 |
SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology
| 39 |
neurips
| 19 | 3 |
2023-06-16 15:12:26.321000
|
https://github.com/MIT-AI-Accelerator/neurips-2020-sevir
| 55 |
Sevir: A storm event imagery dataset for deep learning applications in radar and satellite meteorology
|
https://scholar.google.com/scholar?cluster=8777075661534579096&hl=en&as_sdt=0,5
| 9 | 2,020 |
High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization
| 12 |
neurips
| 3 | 0 |
2023-06-16 15:12:26.633000
|
https://github.com/facebookresearch/ContextualBO
| 13 |
High-dimensional contextual policy search with unknown context rewards using Bayesian optimization
|
https://scholar.google.com/scholar?cluster=13229202016902486124&hl=en&as_sdt=0,36
| 7 | 2,020 |
Model Fusion via Optimal Transport
| 83 |
neurips
| 15 | 4 |
2023-06-16 15:12:26.837000
|
https://github.com/sidak/otfusion
| 72 |
Model fusion via optimal transport
|
https://scholar.google.com/scholar?cluster=4296035737617171484&hl=en&as_sdt=0,14
| 4 | 2,020 |
Learning Individually Inferred Communication for Multi-Agent Cooperation
| 66 |
neurips
| 11 | 2 |
2023-06-16 15:12:27.030000
|
https://github.com/PKU-AI-Edge/I2C
| 31 |
Learning individually inferred communication for multi-agent cooperation
|
https://scholar.google.com/scholar?cluster=1670323167364350618&hl=en&as_sdt=0,5
| 1 | 2,020 |
Set2Graph: Learning Graphs From Sets
| 35 |
neurips
| 6 | 0 |
2023-06-16 15:12:27.224000
|
https://github.com/hadarser/SetToGraphPaper
| 19 |
Set2graph: Learning graphs from sets
|
https://scholar.google.com/scholar?cluster=3992706616039043484&hl=en&as_sdt=0,47
| 1 | 2,020 |
Graph Random Neural Networks for Semi-Supervised Learning on Graphs
| 228 |
neurips
| 37 | 8 |
2023-06-16 15:12:27.417000
|
https://github.com/Grand20/grand
| 182 |
Graph random neural networks for semi-supervised learning on graphs
|
https://scholar.google.com/scholar?cluster=2995656499437981589&hl=en&as_sdt=0,11
| 3 | 2,020 |
Gradient Boosted Normalizing Flows
| 5 |
neurips
| 3 | 4 |
2023-06-16 15:12:27.610000
|
https://github.com/robert-giaquinto/gradient-boosted-normalizing-flows
| 25 |
Gradient boosted normalizing flows
|
https://scholar.google.com/scholar?cluster=952614259564825666&hl=en&as_sdt=0,10
| 3 | 2,020 |
Open Graph Benchmark: Datasets for Machine Learning on Graphs
| 1,349 |
neurips
| 397 | 17 |
2023-06-16 15:12:27.804000
|
https://github.com/snap-stanford/ogb
| 1,685 |
Open graph benchmark: Datasets for machine learning on graphs
|
https://scholar.google.com/scholar?cluster=4143980941711296523&hl=en&as_sdt=0,44
| 42 | 2,020 |
Texture Interpolation for Probing Visual Perception
| 13 |
neurips
| 0 | 0 |
2023-06-16 15:12:28.002000
|
https://github.com/JonathanVacher/texture-interpolation
| 4 |
Texture interpolation for probing visual perception
|
https://scholar.google.com/scholar?cluster=7728700650682598427&hl=en&as_sdt=0,5
| 1 | 2,020 |
Hierarchical Neural Architecture Search for Deep Stereo Matching
| 216 |
neurips
| 50 | 13 |
2023-06-16 15:12:28.196000
|
https://github.com/XuelianCheng/LEAStereo
| 246 |
Hierarchical neural architecture search for deep stereo matching
|
https://scholar.google.com/scholar?cluster=16363724602040348057&hl=en&as_sdt=0,43
| 4 | 2,020 |
Auditing Differentially Private Machine Learning: How Private is Private SGD?
| 114 |
neurips
| 4 | 2 |
2023-06-16 15:12:28.389000
|
https://github.com/jagielski/auditing-dpsgd
| 26 |
Auditing differentially private machine learning: How private is private sgd?
|
https://scholar.google.com/scholar?cluster=281241057337328648&hl=en&as_sdt=0,33
| 3 | 2,020 |
Measuring Systematic Generalization in Neural Proof Generation with Transformers
| 41 |
neurips
| 0 | 0 |
2023-06-16 15:12:28.584000
|
https://github.com/NicolasAG/SGinPG
| 8 |
Measuring systematic generalization in neural proof generation with transformers
|
https://scholar.google.com/scholar?cluster=8849018836826676230&hl=en&as_sdt=0,33
| 2 | 2,020 |
Big Self-Supervised Models are Strong Semi-Supervised Learners
| 1,567 |
neurips
| 570 | 69 |
2023-06-16 15:12:28.777000
|
https://github.com/google-research/simclr
| 3,562 |
Big self-supervised models are strong semi-supervised learners
|
https://scholar.google.com/scholar?cluster=18105628451996555050&hl=en&as_sdt=0,5
| 46 | 2,020 |
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
| 32 |
neurips
| 0 | 0 |
2023-06-16 15:12:28.971000
|
https://github.com/gpleiss/ciq_experiments
| 2 |
Fast matrix square roots with applications to Gaussian processes and Bayesian optimization
|
https://scholar.google.com/scholar?cluster=4177384831232294846&hl=en&as_sdt=0,41
| 1 | 2,020 |
Model Class Reliance for Random Forests
| 16 |
neurips
| 4 | 0 |
2023-06-16 15:12:29.166000
|
https://github.com/gavin-s-smith/mcrforest
| 4 |
Model class reliance for random forests
|
https://scholar.google.com/scholar?cluster=4402509966168777669&hl=en&as_sdt=0,25
| 2 | 2,020 |
Learning to Adapt to Evolving Domains
| 39 |
neurips
| 7 | 4 |
2023-06-16 15:12:29.390000
|
https://github.com/Liuhong99/EAML
| 25 |
Learning to adapt to evolving domains
|
https://scholar.google.com/scholar?cluster=16226509627178633585&hl=en&as_sdt=0,44
| 3 | 2,020 |
Synthesizing Tasks for Block-based Programming
| 7 |
neurips
| 2 | 0 |
2023-06-16 15:12:29.584000
|
https://github.com/adishs/neurips2020_synthesizing-tasks_code
| 1 |
Synthesizing tasks for block-based programming
|
https://scholar.google.com/scholar?cluster=16452730924427259118&hl=en&as_sdt=0,21
| 1 | 2,020 |
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks
| 25 |
neurips
| 3 | 1 |
2023-06-16 15:12:29.787000
|
https://github.com/klightz/Firefly
| 27 |
Firefly neural architecture descent: a general approach for growing neural networks
|
https://scholar.google.com/scholar?cluster=13122447831516243168&hl=en&as_sdt=0,15
| 1 | 2,020 |
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