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A New Distribution on the Simplex with Auto-Encoding Applications | 3 | neurips | 2 | 6 | 2023-06-15 23:43:10.915000 | https://github.com/astirn/MV-Kumaraswamy | 9 | A new distribution on the simplex with auto-encoding applications | https://scholar.google.com/scholar?cluster=3624843939474502459&hl=en&as_sdt=0,5 | 1 | 2,019 |
Model Selection for Contextual Bandits | 75 | neurips | 11 | 1 | 2023-06-15 23:43:11.097000 | https://github.com/akshaykr/oracle_cb | 28 | Model selection for contextual bandits | https://scholar.google.com/scholar?cluster=604693572400865214&hl=en&as_sdt=0,5 | 6 | 2,019 |
FreeAnchor: Learning to Match Anchors for Visual Object Detection | 306 | neurips | 113 | 15 | 2023-06-15 23:43:11.280000 | https://github.com/zhangxiaosong18/FreeAnchor | 670 | Freeanchor: Learning to match anchors for visual object detection | https://scholar.google.com/scholar?cluster=8989326398890700545&hl=en&as_sdt=0,5 | 21 | 2,019 |
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems | 1,343 | neurips | 286 | 72 | 2023-06-15 23:43:11.462000 | https://github.com/nyu-mll/jiant | 1,526 | Superglue: A stickier benchmark for general-purpose language understanding systems | https://scholar.google.com/scholar?cluster=12169300718787849246&hl=en&as_sdt=0,5 | 47 | 2,019 |
Glyce: Glyph-vectors for Chinese Character Representations | 155 | neurips | 73 | 31 | 2023-06-15 23:43:11.646000 | https://github.com/ShannonAI/glyce | 400 | Glyce: Glyph-vectors for chinese character representations | https://scholar.google.com/scholar?cluster=12813244310394658475&hl=en&as_sdt=0,50 | 12 | 2,019 |
General E(2)-Equivariant Steerable CNNs | 319 | neurips | 69 | 6 | 2023-06-15 23:43:11.828000 | https://github.com/QUVA-Lab/e2cnn | 511 | General e (2)-equivariant steerable cnns | https://scholar.google.com/scholar?cluster=11235150486117594383&hl=en&as_sdt=0,14 | 18 | 2,019 |
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets | 67 | neurips | 0 | 0 | 2023-06-15 23:43:12.010000 | https://github.com/RohithKuditipudi/mode-connectivity | 0 | Explaining landscape connectivity of low-cost solutions for multilayer nets | https://scholar.google.com/scholar?cluster=11853008675817475458&hl=en&as_sdt=0,33 | 1 | 2,019 |
Limitations of the empirical Fisher approximation for natural gradient descent | 139 | neurips | 5 | 0 | 2023-06-15 23:43:12.192000 | https://github.com/fkunstner/limitations-empirical-fisher | 42 | Limitations of the empirical Fisher approximation for natural gradient descent | https://scholar.google.com/scholar?cluster=7342864390936584496&hl=en&as_sdt=0,33 | 5 | 2,019 |
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression | 31 | neurips | 1 | 0 | 2023-06-15 23:43:12.374000 | https://github.com/utoronto-theory/pIRLS | 8 | Fast, provably convergent irls algorithm for p-norm linear regression | https://scholar.google.com/scholar?cluster=4351185537881682779&hl=en&as_sdt=0,36 | 4 | 2,019 |
A Model to Search for Synthesizable Molecules | 84 | neurips | 23 | 11 | 2023-06-15 23:43:12.556000 | https://github.com/john-bradshaw/molecule-chef | 73 | A model to search for synthesizable molecules | https://scholar.google.com/scholar?cluster=11917452358715261450&hl=en&as_sdt=0,33 | 5 | 2,019 |
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness | 23 | neurips | 3 | 0 | 2023-06-15 23:43:12.738000 | https://github.com/xiaozhanguva/Measure-Concentration | 7 | Empirically measuring concentration: Fundamental limits on intrinsic robustness | https://scholar.google.com/scholar?cluster=2460203345511372640&hl=en&as_sdt=0,5 | 2 | 2,019 |
Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries | 24 | neurips | 3 | 0 | 2023-06-15 23:43:12.921000 | https://github.com/uvavision/DrillDown | 11 | Drill-down: Interactive retrieval of complex scenes using natural language queries | https://scholar.google.com/scholar?cluster=15992977486578029861&hl=en&as_sdt=0,33 | 7 | 2,019 |
Fast and Accurate Least-Mean-Squares Solvers | 62 | neurips | 11 | 0 | 2023-06-15 23:43:13.103000 | https://github.com/ibramjub/Fast-and-Accurate-Least-Mean-Squares-Solvers | 72 | Fast and accurate least-mean-squares solvers | https://scholar.google.com/scholar?cluster=11022765373503234984&hl=en&as_sdt=0,44 | 4 | 2,019 |
Graph Agreement Models for Semi-Supervised Learning | 55 | neurips | 193 | 1 | 2023-06-15 23:43:13.285000 | https://github.com/tensorflow/neural-structured-learning | 967 | Graph agreement models for semi-supervised learning | https://scholar.google.com/scholar?cluster=17001131817438418296&hl=en&as_sdt=0,5 | 48 | 2,019 |
A Kernel Loss for Solving the Bellman Equation | 59 | neurips | 0 | 1 | 2023-06-15 23:43:13.467000 | https://github.com/lewisKit/Kernel-Bellman-Loss | 0 | A kernel loss for solving the bellman equation | https://scholar.google.com/scholar?cluster=2203690645569443989&hl=en&as_sdt=0,25 | 2 | 2,019 |
AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling | 12 | neurips | 1 | 0 | 2023-06-15 23:43:13.657000 | https://github.com/gbc16/AGEM | 3 | Agem: Solving linear inverse problems via deep priors and sampling | https://scholar.google.com/scholar?cluster=5796954409607252223&hl=en&as_sdt=0,5 | 1 | 2,019 |
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning | 3 | neurips | 0 | 0 | 2023-06-15 23:43:13.839000 | https://github.com/hci-unihd/Probabilistic_Watershed | 8 | Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning | https://scholar.google.com/scholar?cluster=9919550398432186214&hl=en&as_sdt=0,5 | 2 | 2,019 |
Learning Robust Options by Conditional Value at Risk Optimization | 19 | neurips | 1 | 0 | 2023-06-15 23:43:14.030000 | https://github.com/TakuyaHiraoka/Learning-Robust-Options-by-Conditional-Value-at-Risk-Optimization | 9 | Learning robust options by conditional value at risk optimization | https://scholar.google.com/scholar?cluster=14168282705388373415&hl=en&as_sdt=0,45 | 3 | 2,019 |
A Generic Acceleration Framework for Stochastic Composite Optimization | 38 | neurips | 1 | 0 | 2023-06-15 23:43:14.212000 | https://github.com/KuluAndrej/NIPS-2019-code | 1 | A generic acceleration framework for stochastic composite optimization | https://scholar.google.com/scholar?cluster=10947919871280582095&hl=en&as_sdt=0,5 | 2 | 2,019 |
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation | 141 | neurips | 47 | 8 | 2023-06-15 23:43:14.395000 | https://github.com/RunzheYang/MORL | 189 | A generalized algorithm for multi-objective reinforcement learning and policy adaptation | https://scholar.google.com/scholar?cluster=7721047641895252765&hl=en&as_sdt=0,33 | 8 | 2,019 |
Communication trade-offs for Local-SGD with large step size | 40 | neurips | 1 | 0 | 2023-06-15 23:43:14.577000 | https://github.com/kishinmh/Local-SGD | 2 | Communication trade-offs for local-sgd with large step size | https://scholar.google.com/scholar?cluster=16743369759814373109&hl=en&as_sdt=0,5 | 1 | 2,019 |
Towards modular and programmable architecture search | 28 | neurips | 15 | 4 | 2023-06-15 23:43:14.759000 | https://github.com/negrinho/deep_architect | 121 | Towards modular and programmable architecture search | https://scholar.google.com/scholar?cluster=6733031206160413504&hl=en&as_sdt=0,5 | 12 | 2,019 |
Large-scale optimal transport map estimation using projection pursuit | 35 | neurips | 4 | 0 | 2023-06-15 23:43:14.942000 | https://github.com/ChengzijunAixiaoli/PPMM | 13 | Large-scale optimal transport map estimation using projection pursuit | https://scholar.google.com/scholar?cluster=5340124406367691762&hl=en&as_sdt=0,18 | 1 | 2,019 |
Understanding Attention and Generalization in Graph Neural Networks | 205 | neurips | 49 | 1 | 2023-06-15 23:43:15.124000 | https://github.com/bknyaz/graph_attention_pool | 263 | Understanding attention and generalization in graph neural networks | https://scholar.google.com/scholar?cluster=9139711807100164053&hl=en&as_sdt=0,39 | 8 | 2,019 |
Twin Auxilary Classifiers GAN | 64 | neurips | 13 | 3 | 2023-06-15 23:43:15.307000 | https://github.com/batmanlab/twin_ac | 47 | Twin auxilary classifiers gan | https://scholar.google.com/scholar?cluster=6377027598993488889&hl=en&as_sdt=0,23 | 1 | 2,019 |
Online Prediction of Switching Graph Labelings with Cluster Specialists | 3 | neurips | 0 | 0 | 2023-06-15 23:43:15.489000 | https://github.com/jamesro/cluster-specialists | 0 | Online prediction of switching graph labelings with cluster specialists | https://scholar.google.com/scholar?cluster=7730779833279774550&hl=en&as_sdt=0,47 | 2 | 2,019 |
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters | 127 | neurips | 0 | 1 | 2023-06-15 23:43:15.670000 | https://github.com/xxshdw/auto_prune | 6 | Autoprune: Automatic network pruning by regularizing auxiliary parameters | https://scholar.google.com/scholar?cluster=11406488290397197193&hl=en&as_sdt=0,5 | 0 | 2,019 |
Understanding the Role of Momentum in Stochastic Gradient Methods | 72 | neurips | 3 | 0 | 2023-06-15 23:43:15.852000 | https://github.com/Kipok/understanding-momentum | 14 | Understanding the role of momentum in stochastic gradient methods | https://scholar.google.com/scholar?cluster=10334362605827292159&hl=en&as_sdt=0,5 | 2 | 2,019 |
DAC: The Double Actor-Critic Architecture for Learning Options | 47 | neurips | 658 | 6 | 2023-06-15 23:43:16.035000 | https://github.com/ShangtongZhang/DeepRL | 2,943 | DAC: The double actor-critic architecture for learning options | https://scholar.google.com/scholar?cluster=6317609422653411407&hl=en&as_sdt=0,43 | 93 | 2,019 |
Learning from Label Proportions with Generative Adversarial Networks | 26 | neurips | 1 | 1 | 2023-06-15 23:43:16.217000 | https://github.com/liujiabin008/LLP-GAN | 8 | Learning from label proportions with generative adversarial networks | https://scholar.google.com/scholar?cluster=12276305081354929369&hl=en&as_sdt=0,5 | 3 | 2,019 |
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation | 22 | neurips | 1 | 0 | 2023-06-15 23:43:16.400000 | https://github.com/TURuibo/Neuropathic-Pain-Diagnosis-Simulator | 7 | Neuropathic pain diagnosis simulator for causal discovery algorithm evaluation | https://scholar.google.com/scholar?cluster=3595858583853803295&hl=en&as_sdt=0,5 | 3 | 2,019 |
Budgeted Reinforcement Learning in Continuous State Space | 18 | neurips | 127 | 29 | 2023-06-15 23:43:16.581000 | https://github.com/eleurent/rl-agents | 455 | Budgeted reinforcement learning in continuous state space | https://scholar.google.com/scholar?cluster=1156851409573476480&hl=en&as_sdt=0,33 | 20 | 2,019 |
Parameter elimination in particle Gibbs sampling | 11 | neurips | 1 | 0 | 2023-06-15 23:43:16.764000 | https://github.com/uu-sml/neurips2019-parameter-elimination | 5 | Parameter elimination in particle Gibbs sampling | https://scholar.google.com/scholar?cluster=11832975274278617749&hl=en&as_sdt=0,50 | 7 | 2,019 |
Understanding Sparse JL for Feature Hashing | 22 | neurips | 0 | 0 | 2023-06-15 23:43:16.946000 | https://github.com/mjagadeesan/sparsejl-featurehashing | 4 | Understanding sparse JL for feature hashing | https://scholar.google.com/scholar?cluster=13523913285751839530&hl=en&as_sdt=0,10 | 1 | 2,019 |
Planning in entropy-regularized Markov decision processes and games | 18 | neurips | 1 | 0 | 2023-06-15 23:43:17.128000 | https://github.com/omardrwch/smoothcruiser-check | 1 | Planning in entropy-regularized Markov decision processes and games | https://scholar.google.com/scholar?cluster=18118594423877089336&hl=en&as_sdt=0,19 | 2 | 2,019 |
Dynamic Local Regret for Non-convex Online Forecasting | 10 | neurips | 2 | 0 | 2023-06-15 23:43:17.310000 | https://github.com/Timbasa/Dynamic_Local_Regret_for_Non-convex_Online_Forecasting_NeurIPS2019 | 8 | Dynamic local regret for non-convex online forecasting | https://scholar.google.com/scholar?cluster=6302409167507525072&hl=en&as_sdt=0,5 | 4 | 2,019 |
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation | 93 | neurips | 10 | 3 | 2023-06-15 23:43:17.492000 | https://github.com/felixykliu/NAOMI | 46 | Naomi: Non-autoregressive multiresolution sequence imputation | https://scholar.google.com/scholar?cluster=5654873960381975776&hl=en&as_sdt=0,5 | 2 | 2,019 |
Conformalized Quantile Regression | 299 | neurips | 38 | 4 | 2023-06-15 23:43:17.674000 | https://github.com/yromano/cqr | 170 | Conformalized quantile regression | https://scholar.google.com/scholar?cluster=5581207407270823451&hl=en&as_sdt=0,5 | 8 | 2,019 |
MarginGAN: Adversarial Training in Semi-Supervised Learning | 36 | neurips | 2 | 0 | 2023-06-15 23:43:17.857000 | https://github.com/xdu-DJhao/MarginGAN | 9 | MarginGAN: adversarial training in semi-supervised learning | https://scholar.google.com/scholar?cluster=6031857058045286818&hl=en&as_sdt=0,5 | 1 | 2,019 |
Cold Case: The Lost MNIST Digits | 101 | neurips | 32 | 0 | 2023-06-15 23:43:18.040000 | https://github.com/facebookresearch/qmnist | 231 | Cold case: The lost mnist digits | https://scholar.google.com/scholar?cluster=9918380668226002925&hl=en&as_sdt=0,5 | 12 | 2,019 |
RUBi: Reducing Unimodal Biases for Visual Question Answering | 267 | neurips | 15 | 3 | 2023-06-15 23:43:18.222000 | https://github.com/cdancette/rubi.bootstrap.pytorch | 57 | Rubi: Reducing unimodal biases for visual question answering | https://scholar.google.com/scholar?cluster=3200511868750352559&hl=en&as_sdt=0,49 | 5 | 2,019 |
Learning Sample-Specific Models with Low-Rank Personalized Regression | 12 | neurips | 2 | 1 | 2023-06-15 23:43:18.404000 | https://github.com/blengerich/personalized_regression | 15 | Learning sample-specific models with low-rank personalized regression | https://scholar.google.com/scholar?cluster=9544461235321687427&hl=en&as_sdt=0,43 | 6 | 2,019 |
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration | 23 | neurips | 0 | 1 | 2023-06-15 23:43:18.586000 | https://github.com/drgrhm/alg_config | 1 | Procrastinating with confidence: Near-optimal, anytime, adaptive algorithm configuration | https://scholar.google.com/scholar?cluster=12402924190582219171&hl=en&as_sdt=0,5 | 1 | 2,019 |
Unsupervised Scalable Representation Learning for Multivariate Time Series | 236 | neurips | 84 | 0 | 2023-06-15 23:43:18.769000 | https://github.com/White-Link/UnsupervisedScalableRepresentationLearningTimeSeries | 340 | Unsupervised scalable representation learning for multivariate time series | https://scholar.google.com/scholar?cluster=1013253939456705166&hl=en&as_sdt=0,31 | 17 | 2,019 |
Total Least Squares Regression in Input Sparsity Time | 8 | neurips | 6 | 0 | 2023-06-15 23:43:18.951000 | https://github.com/yangxinuw/total_least_squares_code | 4 | Total least squares regression in input sparsity time | https://scholar.google.com/scholar?cluster=976445259821829575&hl=en&as_sdt=0,5 | 1 | 2,019 |
Bayesian Learning of Sum-Product Networks | 34 | neurips | 6 | 3 | 2023-06-15 23:43:19.134000 | https://github.com/trappmartin/BayesianSumProductNetworks | 12 | Bayesian learning of sum-product networks | https://scholar.google.com/scholar?cluster=10871336632487264585&hl=en&as_sdt=0,5 | 3 | 2,019 |
Discriminative Topic Modeling with Logistic LDA | 18 | neurips | 5 | 0 | 2023-06-15 23:43:19.316000 | https://github.com/lucastheis/logistic_lda | 18 | Discriminative topic modeling with logistic LDA | https://scholar.google.com/scholar?cluster=8692276849254224947&hl=en&as_sdt=0,33 | 2 | 2,019 |
Disentangling Influence: Using disentangled representations to audit model predictions | 20 | neurips | 1 | 0 | 2023-06-15 23:43:19.498000 | https://github.com/charliemarx/disentangling-influence | 4 | Disentangling influence: Using disentangled representations to audit model predictions | https://scholar.google.com/scholar?cluster=800319645349031007&hl=en&as_sdt=0,5 | 1 | 2,019 |
Deep Structured Prediction for Facial Landmark Detection | 24 | neurips | 5 | 0 | 2023-06-15 23:43:19.680000 | https://github.com/lisha-chen/Deep-structured-facial-landmark-detection | 18 | Deep structured prediction for facial landmark detection | https://scholar.google.com/scholar?cluster=18147202694366911205&hl=en&as_sdt=0,32 | 3 | 2,019 |
Mutually Regressive Point Processes | 16 | neurips | 0 | 0 | 2023-06-15 23:43:19.862000 | https://github.com/ifiaposto/Mutually-Regressive-Point-Processes | 4 | Mutually regressive point processes | https://scholar.google.com/scholar?cluster=9562149540635904941&hl=en&as_sdt=0,5 | 2 | 2,019 |
Demystifying Black-box Models with Symbolic Metamodels | 73 | neurips | 22 | 1 | 2023-06-15 23:43:20.045000 | https://github.com/ahmedmalaa/Symbolic-Metamodeling | 43 | Demystifying black-box models with symbolic metamodels | https://scholar.google.com/scholar?cluster=4982738822209753358&hl=en&as_sdt=0,33 | 5 | 2,019 |
SHE: A Fast and Accurate Deep Neural Network for Encrypted Data | 76 | neurips | 6 | 2 | 2023-06-15 23:43:20.227000 | https://github.com/qianlou/SHE | 22 | She: A fast and accurate deep neural network for encrypted data | https://scholar.google.com/scholar?cluster=13256787420791403235&hl=en&as_sdt=0,5 | 1 | 2,019 |
Competitive Gradient Descent | 95 | neurips | 2 | 0 | 2023-06-15 23:43:20.409000 | https://github.com/f-t-s/CGD | 22 | Competitive gradient descent | https://scholar.google.com/scholar?cluster=16079761912267834651&hl=en&as_sdt=0,5 | 4 | 2,019 |
Arbicon-Net: Arbitrary Continuous Geometric Transformation Networks for Image Registration | 25 | neurips | 3 | 1 | 2023-06-15 23:43:20.610000 | https://github.com/nyummvc/Arbicon-Net | 15 | Arbicon-net: Arbitrary continuous geometric transformation networks for image registration | https://scholar.google.com/scholar?cluster=7779525469156132489&hl=en&as_sdt=0,33 | 4 | 2,019 |
Point-Voxel CNN for Efficient 3D Deep Learning | 456 | neurips | 126 | 0 | 2023-06-15 23:43:20.799000 | https://github.com/mit-han-lab/pvcnn | 556 | Point-voxel cnn for efficient 3d deep learning | https://scholar.google.com/scholar?cluster=10002989291325329256&hl=en&as_sdt=0,48 | 24 | 2,019 |
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization | 68 | neurips | 7 | 0 | 2023-06-15 23:43:20.982000 | https://github.com/KaidiXu/ZO-AdaMM | 22 | Zo-adamm: Zeroth-order adaptive momentum method for black-box optimization | https://scholar.google.com/scholar?cluster=410761263442584539&hl=en&as_sdt=0,33 | 2 | 2,019 |
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging | 162 | neurips | 54 | 3 | 2023-06-15 23:43:21.164000 | https://github.com/perslev/U-Time | 201 | U-time: A fully convolutional network for time series segmentation applied to sleep staging | https://scholar.google.com/scholar?cluster=8255933860376596525&hl=en&as_sdt=0,21 | 8 | 2,019 |
Meta-Curvature | 106 | neurips | 1 | 2 | 2023-06-15 23:43:21.346000 | https://github.com/silverbottlep/meta_curvature | 9 | Meta-curvature | https://scholar.google.com/scholar?cluster=8144207372117342162&hl=en&as_sdt=0,5 | 4 | 2,019 |
Exploration via Hindsight Goal Generation | 57 | neurips | 8 | 0 | 2023-06-15 23:43:21.533000 | https://github.com/Stilwell-Git/Hindsight-Goal-Generation | 24 | Exploration via hindsight goal generation | https://scholar.google.com/scholar?cluster=15515347371168435712&hl=en&as_sdt=0,33 | 3 | 2,019 |
VIREL: A Variational Inference Framework for Reinforcement Learning | 40 | neurips | 5 | 1 | 2023-06-15 23:43:21.724000 | https://github.com/AnujMahajanOxf/VIREL | 15 | Virel: A variational inference framework for reinforcement learning | https://scholar.google.com/scholar?cluster=3837224869714850766&hl=en&as_sdt=0,33 | 2 | 2,019 |
Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration | 31 | neurips | 3 | 0 | 2023-06-15 23:43:21.907000 | https://github.com/jliang993/A3DMM | 8 | Trajectory of alternating direction method of multipliers and adaptive acceleration | https://scholar.google.com/scholar?cluster=8586222791731543519&hl=en&as_sdt=0,26 | 2 | 2,019 |
Focused Quantization for Sparse CNNs | 25 | neurips | 20 | 4 | 2023-06-15 23:43:22.095000 | https://github.com/deep-fry/mayo | 109 | Focused quantization for sparse CNNs | https://scholar.google.com/scholar?cluster=5764391117070924493&hl=en&as_sdt=0,10 | 11 | 2,019 |
Knowledge Extraction with No Observable Data | 75 | neurips | 10 | 0 | 2023-06-15 23:43:22.277000 | https://github.com/snudatalab/KegNet | 37 | Knowledge extraction with no observable data | https://scholar.google.com/scholar?cluster=3775105952512776839&hl=en&as_sdt=0,5 | 4 | 2,019 |
Global Guarantees for Blind Demodulation with Generative Priors | 28 | neurips | 0 | 0 | 2023-06-15 23:43:22.460000 | https://github.com/babhrujoshi/Blind_demod_gen_prior | 2 | Global guarantees for blind demodulation with generative priors | https://scholar.google.com/scholar?cluster=49698119456763508&hl=en&as_sdt=0,32 | 1 | 2,019 |
Neural Jump Stochastic Differential Equations | 182 | neurips | 17 | 0 | 2023-06-15 23:43:22.642000 | https://github.com/000Justin000/torchdiffeq | 45 | Neural jump stochastic differential equations | https://scholar.google.com/scholar?cluster=14697126289882105767&hl=en&as_sdt=0,33 | 4 | 2,019 |
Learning about an exponential amount of conditional distributions | 26 | neurips | 0 | 0 | 2023-06-15 23:43:22.824000 | https://github.com/IshmaelBelghazi/learning_an_exponential_amount_of_conditional_distributions | 0 | Learning about an exponential amount of conditional distributions | https://scholar.google.com/scholar?cluster=15393166666132601264&hl=en&as_sdt=0,48 | 2 | 2,019 |
Multi-mapping Image-to-Image Translation via Learning Disentanglement | 92 | neurips | 15 | 1 | 2023-06-15 23:43:23.007000 | https://github.com/Xiaoming-Yu/DMIT | 110 | Multi-mapping image-to-image translation via learning disentanglement | https://scholar.google.com/scholar?cluster=18213035425048385822&hl=en&as_sdt=0,5 | 15 | 2,019 |
Explicitly disentangling image content from translation and rotation with spatial-VAE | 71 | neurips | 18 | 2 | 2023-06-15 23:43:23.189000 | https://github.com/tbepler/spatial-VAE | 55 | Explicitly disentangling image content from translation and rotation with spatial-VAE | https://scholar.google.com/scholar?cluster=6574810273867158367&hl=en&as_sdt=0,14 | 7 | 2,019 |
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies | 149 | neurips | 1 | 0 | 2023-06-15 23:43:23.372000 | https://github.com/ykasten/Convergence-Rate-NN-Different-Frequencies | 6 | The convergence rate of neural networks for learned functions of different frequencies | https://scholar.google.com/scholar?cluster=12179223750271364799&hl=en&as_sdt=0,23 | 5 | 2,019 |
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem | 125 | neurips | 1 | 0 | 2023-06-15 23:43:23.554000 | https://github.com/gomena/statistical_bounds_entropic_OT | 1 | Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem | https://scholar.google.com/scholar?cluster=6105913006833342284&hl=en&as_sdt=0,44 | 1 | 2,019 |
A Game Theoretic Approach to Class-wise Selective Rationalization | 47 | neurips | 3 | 5 | 2023-06-15 23:43:23.736000 | https://github.com/code-terminator/classwise_rationale | 12 | A game theoretic approach to class-wise selective rationalization | https://scholar.google.com/scholar?cluster=8292388829309690879&hl=en&as_sdt=0,21 | 4 | 2,019 |
Variational Bayesian Decision-making for Continuous Utilities | 18 | neurips | 1 | 0 | 2023-06-15 23:43:23.920000 | https://github.com/tkusmierczyk/lcvi | 4 | Variational Bayesian decision-making for continuous utilities | https://scholar.google.com/scholar?cluster=2997507484784259646&hl=en&as_sdt=0,5 | 2 | 2,019 |
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning | 204 | neurips | 7,320 | 1,025 | 2023-06-15 23:43:24.103000 | https://github.com/google-research/google-research | 29,776 | Search on the replay buffer: Bridging planning and reinforcement learning | https://scholar.google.com/scholar?cluster=17777579381680460522&hl=en&as_sdt=0,5 | 727 | 2,019 |
Transductive Zero-Shot Learning with Visual Structure Constraint | 72 | neurips | 9 | 1 | 2023-06-15 23:43:24.285000 | https://github.com/raywzy/VSC | 42 | Transductive zero-shot learning with visual structure constraint | https://scholar.google.com/scholar?cluster=14823968865961413196&hl=en&as_sdt=0,10 | 2 | 2,019 |
Implicit Regularization for Optimal Sparse Recovery | 63 | neurips | 1 | 0 | 2023-06-15 23:43:24.467000 | https://github.com/TomasVaskevicius/implicit_sparsity_neurips2019 | 3 | Implicit regularization for optimal sparse recovery | https://scholar.google.com/scholar?cluster=6600835910528488334&hl=en&as_sdt=0,5 | 3 | 2,019 |
Residual Flows for Invertible Generative Modeling | 279 | neurips | 44 | 3 | 2023-06-15 23:43:24.649000 | https://github.com/rtqichen/residual-flows | 251 | Residual flows for invertible generative modeling | https://scholar.google.com/scholar?cluster=13099094504334344711&hl=en&as_sdt=0,5 | 12 | 2,019 |
Adversarial Training and Robustness for Multiple Perturbations | 309 | neurips | 8 | 16 | 2023-06-15 23:43:24.831000 | https://github.com/ftramer/MultiRobustness | 46 | Adversarial training and robustness for multiple perturbations | https://scholar.google.com/scholar?cluster=6630235695392252264&hl=en&as_sdt=0,22 | 2 | 2,019 |
Stein Variational Gradient Descent With Matrix-Valued Kernels | 54 | neurips | 1 | 0 | 2023-06-15 23:43:25.013000 | https://github.com/dilinwang820/matrix_svgd | 8 | Stein variational gradient descent with matrix-valued kernels | https://scholar.google.com/scholar?cluster=6300168546020464188&hl=en&as_sdt=0,10 | 3 | 2,019 |
Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes | 140 | neurips | 22 | 1 | 2023-06-15 23:43:25.195000 | https://github.com/thegregyang/GP4A | 220 | Wide feedforward or recurrent neural networks of any architecture are gaussian processes | https://scholar.google.com/scholar?cluster=13759507907397409226&hl=en&as_sdt=0,5 | 9 | 2,019 |
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums | 37 | neurips | 2 | 0 | 2023-06-15 23:43:25.378000 | https://github.com/HadrienHx/ADFS_NeurIPS | 1 | An accelerated decentralized stochastic proximal algorithm for finite sums | https://scholar.google.com/scholar?cluster=748231720425736301&hl=en&as_sdt=0,5 | 1 | 2,019 |
Data Cleansing for Models Trained with SGD | 51 | neurips | 6 | 1 | 2023-06-15 23:43:25.560000 | https://github.com/sato9hara/sgd-influence | 48 | Data cleansing for models trained with SGD | https://scholar.google.com/scholar?cluster=11335556309506749393&hl=en&as_sdt=0,5 | 2 | 2,019 |
Generating Diverse High-Fidelity Images with VQ-VAE-2 | 1,024 | neurips | 1,360 | 34 | 2023-06-15 23:43:25.742000 | https://github.com/deepmind/sonnet | 9,571 | Generating diverse high-fidelity images with vq-vae-2 | https://scholar.google.com/scholar?cluster=7339215229612384474&hl=en&as_sdt=0,5 | 425 | 2,019 |
When to Trust Your Model: Model-Based Policy Optimization | 611 | neurips | 77 | 18 | 2023-06-15 23:43:25.924000 | https://github.com/JannerM/mbpo | 416 | When to trust your model: Model-based policy optimization | https://scholar.google.com/scholar?cluster=4248859125840907707&hl=en&as_sdt=0,39 | 10 | 2,019 |
On Making Stochastic Classifiers Deterministic | 23 | neurips | 7,320 | 1,025 | 2023-06-15 23:43:26.106000 | https://github.com/google-research/google-research | 29,776 | On making stochastic classifiers deterministic | https://scholar.google.com/scholar?cluster=9514965586959557733&hl=en&as_sdt=0,39 | 727 | 2,019 |
Blind Super-Resolution Kernel Estimation using an Internal-GAN | 323 | neurips | 73 | 38 | 2023-06-15 23:43:26.288000 | https://github.com/sefibk/KernelGAN | 312 | Blind super-resolution kernel estimation using an internal-gan | https://scholar.google.com/scholar?cluster=248352425941813595&hl=en&as_sdt=0,5 | 8 | 2,019 |
Learning to Learn By Self-Critique | 66 | neurips | 7 | 3 | 2023-06-15 23:43:26.471000 | https://github.com/AntreasAntoniou/Learning_to_Learn_via_Self-Critique | 44 | Learning to learn by self-critique | https://scholar.google.com/scholar?cluster=1091119097992623438&hl=en&as_sdt=0,33 | 6 | 2,019 |
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses | 33 | neurips | 0 | 0 | 2023-06-15 23:43:26.652000 | https://github.com/umarteau/Newton-Method-for-GSC-losses- | 3 | Globally convergent newton methods for ill-conditioned generalized self-concordant losses | https://scholar.google.com/scholar?cluster=4570728101284672632&hl=en&as_sdt=0,3 | 3 | 2,019 |
Is Deeper Better only when Shallow is Good? | 37 | neurips | 0 | 0 | 2023-06-15 23:43:26.835000 | https://github.com/emalach/IsDeeperBetter | 0 | Is deeper better only when shallow is good? | https://scholar.google.com/scholar?cluster=8541069837961005267&hl=en&as_sdt=0,44 | 1 | 2,019 |
The Thermodynamic Variational Objective | 36 | neurips | 0 | 0 | 2023-06-15 23:43:27.017000 | https://github.com/vmasrani/tvo | 0 | The thermodynamic variational objective | https://scholar.google.com/scholar?cluster=8303803537398982071&hl=en&as_sdt=0,5 | 0 | 2,019 |
Sampling Sketches for Concave Sublinear Functions of Frequencies | 6 | neurips | 0 | 0 | 2023-06-15 23:43:27.199000 | https://github.com/ofirgeri/concave-sublinear-sampling | 0 | Sampling sketches for concave sublinear functions of frequencies | https://scholar.google.com/scholar?cluster=5473104206629301806&hl=en&as_sdt=0,4 | 1 | 2,019 |
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss | 962 | neurips | 111 | 12 | 2023-06-15 23:43:27.381000 | https://github.com/kaidic/LDAM-DRW | 569 | Learning imbalanced datasets with label-distribution-aware margin loss | https://scholar.google.com/scholar?cluster=14488921758498385858&hl=en&as_sdt=0,5 | 15 | 2,019 |
Multivariate Triangular Quantile Maps for Novelty Detection | 18 | neurips | 3 | 5 | 2023-06-15 23:43:27.564000 | https://github.com/GinGinWang/MTQ | 7 | Multivariate triangular quantile maps for novelty detection | https://scholar.google.com/scholar?cluster=7987123893251995250&hl=en&as_sdt=0,5 | 3 | 2,019 |
Gradient-based Adaptive Markov Chain Monte Carlo | 23 | neurips | 5 | 0 | 2023-06-15 23:43:27.746000 | https://github.com/mtitsias/gadMCMC | 21 | Gradient-based adaptive markov chain monte carlo | https://scholar.google.com/scholar?cluster=13990086497515936909&hl=en&as_sdt=0,18 | 3 | 2,019 |
Online Forecasting of Total-Variation-bounded Sequences | 34 | neurips | 0 | 0 | 2023-06-15 23:43:27.928000 | https://github.com/yuxiangw/tv_online | 3 | Online forecasting of total-variation-bounded sequences | https://scholar.google.com/scholar?cluster=1207130136020942361&hl=en&as_sdt=0,33 | 2 | 2,019 |
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video | 386 | neurips | 52 | 15 | 2023-06-15 23:43:28.111000 | https://github.com/JiawangBian/sc_depth_pl | 293 | Unsupervised scale-consistent depth and ego-motion learning from monocular video | https://scholar.google.com/scholar?cluster=1362055635586007597&hl=en&as_sdt=0,5 | 10 | 2,019 |
Variational Denoising Network: Toward Blind Noise Modeling and Removal | 242 | neurips | 45 | 3 | 2023-06-15 23:43:28.292000 | https://github.com/zsyOAOA/VDNet | 194 | Variational denoising network: Toward blind noise modeling and removal | https://scholar.google.com/scholar?cluster=18313022457936123811&hl=en&as_sdt=0,5 | 3 | 2,019 |
Multi-task Learning for Aggregated Data using Gaussian Processes | 25 | neurips | 1 | 0 | 2023-06-15 23:43:28.475000 | https://github.com/frb-yousefi/aggregated-multitask-gp | 10 | Multi-task learning for aggregated data using Gaussian processes | https://scholar.google.com/scholar?cluster=9068915187307088687&hl=en&as_sdt=0,33 | 2 | 2,019 |
Efficient characterization of electrically evoked responses for neural interfaces | 5 | neurips | 0 | 0 | 2023-06-15 23:43:28.657000 | https://github.com/Chichilnisky-Lab/shah-neurips-2019 | 3 | Efficient characterization of electrically evoked responses for neural interfaces | https://scholar.google.com/scholar?cluster=18237433847770575510&hl=en&as_sdt=0,5 | 8 | 2,019 |
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels | 197 | neurips | 17 | 1 | 2023-06-15 23:43:28.839000 | https://github.com/KangchengHou/gntk | 94 | Graph neural tangent kernel: Fusing graph neural networks with graph kernels | https://scholar.google.com/scholar?cluster=7700085274406978551&hl=en&as_sdt=0,23 | 6 | 2,019 |
Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces | 40 | neurips | 3 | 0 | 2023-06-15 23:43:29.021000 | https://github.com/wangbx66/differentially-private-q-learning | 10 | Privacy-preserving q-learning with functional noise in continuous spaces | https://scholar.google.com/scholar?cluster=253585098814477836&hl=en&as_sdt=0,5 | 2 | 2,019 |
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