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Hyperspherical Prototype Networks | 75 | neurips | 6 | 2 | 2023-06-15 23:42:34.389000 | https://github.com/psmmettes/hpn | 59 | Hyperspherical prototype networks | https://scholar.google.com/scholar?cluster=15240435433337095231&hl=en&as_sdt=0,47 | 3 | 2,019 |
Lower Bounds on Adversarial Robustness from Optimal Transport | 82 | neurips | 0 | 0 | 2023-06-15 23:42:34.572000 | https://github.com/inspire-group/robustness-via-transport | 12 | Lower bounds on adversarial robustness from optimal transport | https://scholar.google.com/scholar?cluster=2678310467137454397&hl=en&as_sdt=0,41 | 4 | 2,019 |
A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution | 33 | neurips | 1 | 0 | 2023-06-15 23:42:34.753000 | https://github.com/qingqu06/MCS-BD | 8 | A nonconvex approach for exact and efficient multichannel sparse blind deconvolution | https://scholar.google.com/scholar?cluster=16270892070562641285&hl=en&as_sdt=0,48 | 2 | 2,019 |
Generalization of Reinforcement Learners with Working and Episodic Memory | 49 | neurips | 16 | 1 | 2023-06-15 23:42:34.935000 | https://github.com/deepmind/dm_memorytasks | 222 | Generalization of reinforcement learners with working and episodic memory | https://scholar.google.com/scholar?cluster=15492128596340349153&hl=en&as_sdt=0,5 | 13 | 2,019 |
DTWNet: a Dynamic Time Warping Network | 67 | neurips | 24 | 0 | 2023-06-15 23:42:35.117000 | https://github.com/TideDancer/DTWNet | 61 | Dtwnet: a dynamic time warping network | https://scholar.google.com/scholar?cluster=12755791538559814955&hl=en&as_sdt=0,5 | 4 | 2,019 |
Learning Erdos-Renyi Random Graphs via Edge Detecting Queries | 3 | neurips | 0 | 0 | 2023-06-15 23:42:35.300000 | https://github.com/scarlett-nus/er_edge_det | 1 | Learning erdos-renyi random graphs via edge detecting queries | https://scholar.google.com/scholar?cluster=10593108232555201387&hl=en&as_sdt=0,33 | 1 | 2,019 |
Cormorant: Covariant Molecular Neural Networks | 320 | neurips | 11 | 4 | 2023-06-15 23:42:35.482000 | https://github.com/risilab/cormorant | 51 | Cormorant: Covariant molecular neural networks | https://scholar.google.com/scholar?cluster=8775328101914516140&hl=en&as_sdt=0,6 | 6 | 2,019 |
Explicit Explore-Exploit Algorithms in Continuous State Spaces | 25 | neurips | 1 | 1 | 2023-06-15 23:42:35.663000 | https://github.com/mbhenaff/neural-e3 | 6 | Explicit explore-exploit algorithms in continuous state spaces | https://scholar.google.com/scholar?cluster=12048053736281470251&hl=en&as_sdt=0,43 | 3 | 2,019 |
Spherical Text Embedding | 100 | neurips | 28 | 1 | 2023-06-15 23:42:35.845000 | https://github.com/yumeng5/Spherical-Text-Embedding | 175 | Spherical text embedding | https://scholar.google.com/scholar?cluster=12918153204372090641&hl=en&as_sdt=0,5 | 7 | 2,019 |
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates | 108 | neurips | 0 | 0 | 2023-06-15 23:42:36.028000 | https://github.com/jnegrea/neurips2019-5904-code | 0 | Information-theoretic generalization bounds for SGLD via data-dependent estimates | https://scholar.google.com/scholar?cluster=7753094016128603941&hl=en&as_sdt=0,36 | 2 | 2,019 |
Efficient Algorithms for Smooth Minimax Optimization | 161 | neurips | 1 | 0 | 2023-06-15 23:42:36.210000 | https://github.com/POLane16/DIAG | 2 | Efficient algorithms for smooth minimax optimization | https://scholar.google.com/scholar?cluster=16329029546814043430&hl=en&as_sdt=0,10 | 1 | 2,019 |
Uniform convergence may be unable to explain generalization in deep learning | 203 | neurips | 3 | 0 | 2023-06-15 23:42:36.391000 | https://github.com/locuslab/uniform-convergence-NeurIPS19 | 10 | Uniform convergence may be unable to explain generalization in deep learning | https://scholar.google.com/scholar?cluster=863649597305754781&hl=en&as_sdt=0,5 | 5 | 2,019 |
Robust exploration in linear quadratic reinforcement learning | 30 | neurips | 1 | 0 | 2023-06-15 23:42:36.574000 | https://github.com/umenberger/robust-exploration | 3 | Robust exploration in linear quadratic reinforcement learning | https://scholar.google.com/scholar?cluster=2367192655687750423&hl=en&as_sdt=0,5 | 2 | 2,019 |
Meta-Surrogate Benchmarking for Hyperparameter Optimization | 39 | neurips | 122 | 42 | 2023-06-15 23:42:36.756000 | https://github.com/amzn/emukit | 518 | Meta-surrogate benchmarking for hyperparameter optimization | https://scholar.google.com/scholar?cluster=11453320688261024074&hl=en&as_sdt=0,44 | 17 | 2,019 |
Bayesian Optimization under Heavy-tailed Payoffs | 18 | neurips | 1 | 0 | 2023-06-15 23:42:36.938000 | https://github.com/sayakrc/Bayesian-Optimization-under-Heavy-tailed-Payoffs | 2 | Bayesian optimization under heavy-tailed payoffs | https://scholar.google.com/scholar?cluster=13505569785706603618&hl=en&as_sdt=0,23 | 1 | 2,019 |
Meta-Learning with Implicit Gradients | 611 | neurips | 7 | 3 | 2023-06-15 23:42:37.120000 | https://github.com/aravindr93/imaml_dev | 42 | Meta-learning with implicit gradients | https://scholar.google.com/scholar?cluster=13369476722285367510&hl=en&as_sdt=0,5 | 6 | 2,019 |
Differentially Private Markov Chain Monte Carlo | 20 | neurips | 1 | 0 | 2023-06-15 23:42:37.303000 | https://github.com/DPBayes/DP-MCMC-NeurIPS2019 | 2 | Differentially private markov chain monte carlo | https://scholar.google.com/scholar?cluster=918464932035758284&hl=en&as_sdt=0,34 | 4 | 2,019 |
Universal Boosting Variational Inference | 25 | neurips | 1 | 2 | 2023-06-15 23:42:37.486000 | https://github.com/trevorcampbell/ubvi | 5 | Universal boosting variational inference | https://scholar.google.com/scholar?cluster=8765801192922699610&hl=en&as_sdt=0,5 | 1 | 2,019 |
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning | 119 | neurips | 15 | 6 | 2023-06-15 23:42:37.668000 | https://github.com/yalidu/liir | 53 | Liir: Learning individual intrinsic reward in multi-agent reinforcement learning | https://scholar.google.com/scholar?cluster=17772634861741004001&hl=en&as_sdt=0,11 | 2 | 2,019 |
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits | 9 | neurips | 0 | 0 | 2023-06-15 23:42:37.850000 | https://github.com/wenhao-z/Bayes_factor_Opposite_neuron | 0 | A normative theory for causal inference and Bayes factor computation in neural circuits | https://scholar.google.com/scholar?cluster=17602894246019062673&hl=en&as_sdt=0,5 | 1 | 2,019 |
The Geometry of Deep Networks: Power Diagram Subdivision | 38 | neurips | 1 | 0 | 2023-06-15 23:42:38.033000 | https://github.com/RandallBalestriero/PowerDiagram | 1 | The geometry of deep networks: Power diagram subdivision | https://scholar.google.com/scholar?cluster=3949701883941421755&hl=en&as_sdt=0,5 | 3 | 2,019 |
Semi-Parametric Efficient Policy Learning with Continuous Actions | 42 | neurips | 0 | 0 | 2023-06-15 23:42:38.215000 | https://github.com/vsyrgkanis/policy_learning_continuous_actions | 1 | Semi-parametric efficient policy learning with continuous actions | https://scholar.google.com/scholar?cluster=4715242630767195643&hl=en&as_sdt=0,47 | 2 | 2,019 |
Learning Stable Deep Dynamics Models | 143 | neurips | 10 | 2 | 2023-06-15 23:42:38.397000 | https://github.com/locuslab/stable_dynamics | 25 | Learning stable deep dynamics models | https://scholar.google.com/scholar?cluster=15884383241607994844&hl=en&as_sdt=0,26 | 2 | 2,019 |
Beyond the Single Neuron Convex Barrier for Neural Network Certification | 140 | neurips | 99 | 12 | 2023-06-15 23:42:38.579000 | https://github.com/eth-sri/eran | 284 | Beyond the single neuron convex barrier for neural network certification | https://scholar.google.com/scholar?cluster=17997567581832300594&hl=en&as_sdt=0,11 | 22 | 2,019 |
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models | 166 | neurips | 33 | 2 | 2023-06-15 23:42:38.761000 | https://github.com/iffsid/mmvae | 142 | Variational mixture-of-experts autoencoders for multi-modal deep generative models | https://scholar.google.com/scholar?cluster=204166380229744591&hl=en&as_sdt=0,5 | 8 | 2,019 |
Language as an Abstraction for Hierarchical Deep Reinforcement Learning | 148 | neurips | 12 | 4 | 2023-06-15 23:42:38.944000 | https://github.com/google-research/clevr_robot_env | 118 | Language as an abstraction for hierarchical deep reinforcement learning | https://scholar.google.com/scholar?cluster=13558761030433152437&hl=en&as_sdt=0,33 | 7 | 2,019 |
High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes | 131 | neurips | 10 | 0 | 2023-06-15 23:42:39.126000 | https://github.com/mbohlkeschneider/gluon-ts | 43 | High-dimensional multivariate forecasting with low-rank gaussian copula processes | https://scholar.google.com/scholar?cluster=15568852272532937940&hl=en&as_sdt=0,29 | 1 | 2,019 |
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization | 3 | neurips | 1 | 0 | 2023-06-15 23:42:39.308000 | https://github.com/framinmansour/Learning-Macroscopic-Brain-Connectomes-via-Group-Sparse-Factorization | 6 | Learning macroscopic brain connectomes via group-sparse factorization | https://scholar.google.com/scholar?cluster=18281061878272336341&hl=en&as_sdt=0,5 | 2 | 2,019 |
Combinatorial Inference against Label Noise | 19 | neurips | 0 | 1 | 2023-06-15 23:42:39.490000 | https://github.com/snow12345/Combinatorial_Classification | 7 | Combinatorial inference against label noise | https://scholar.google.com/scholar?cluster=10313449809360280189&hl=en&as_sdt=0,5 | 1 | 2,019 |
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data | 8 | neurips | 5 | 1 | 2023-06-15 23:42:39.672000 | https://github.com/highan911/FLRML | 6 | Fast low-rank metric learning for large-scale and high-dimensional data | https://scholar.google.com/scholar?cluster=5081716944652547266&hl=en&as_sdt=0,33 | 1 | 2,019 |
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent | 772 | neurips | 227 | 58 | 2023-06-15 23:42:39.854000 | https://github.com/google/neural-tangents | 2,023 | Wide neural networks of any depth evolve as linear models under gradient descent | https://scholar.google.com/scholar?cluster=10271588959901500441&hl=en&as_sdt=0,5 | 64 | 2,019 |
Retrosynthesis Prediction with Conditional Graph Logic Network | 124 | neurips | 22 | 6 | 2023-06-15 23:42:40.037000 | https://github.com/Hanjun-Dai/GLN | 99 | Retrosynthesis prediction with conditional graph logic network | https://scholar.google.com/scholar?cluster=13973073530348784019&hl=en&as_sdt=0,5 | 10 | 2,019 |
Efficient Pure Exploration in Adaptive Round model | 13 | neurips | 0 | 0 | 2023-06-15 23:42:40.219000 | https://github.com/jmshi123/mab-nips-2019 | 0 | Efficient pure exploration in adaptive round model | https://scholar.google.com/scholar?cluster=15910693782133163407&hl=en&as_sdt=0,5 | 2 | 2,019 |
Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction | 8 | neurips | 1 | 3 | 2023-06-15 23:42:40.400000 | https://github.com/alaflaquiere/learn-spatial-structure | 1 | Unsupervised emergence of egocentric spatial structure from sensorimotor prediction | https://scholar.google.com/scholar?cluster=14146114987912922308&hl=en&as_sdt=0,31 | 2 | 2,019 |
Generalized Off-Policy Actor-Critic | 44 | neurips | 658 | 6 | 2023-06-15 23:42:40.584000 | https://github.com/ShangtongZhang/DeepRL | 2,943 | Generalized off-policy actor-critic | https://scholar.google.com/scholar?cluster=9029293262524916308&hl=en&as_sdt=0,33 | 93 | 2,019 |
Average Individual Fairness: Algorithms, Generalization and Experiments | 78 | neurips | 0 | 0 | 2023-06-15 23:42:40.767000 | https://github.com/SaeedSharifiMa/AIF | 0 | Average individual fairness: Algorithms, generalization and experiments | https://scholar.google.com/scholar?cluster=8157096146249952889&hl=en&as_sdt=0,5 | 2 | 2,019 |
Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse | 12 | neurips | 2 | 0 | 2023-06-15 23:42:40.950000 | https://github.com/berenslab/abc-ribbon | 2 | Approximate bayesian inference for a mechanistic model of vesicle release at a ribbon synapse | https://scholar.google.com/scholar?cluster=17222924363509946962&hl=en&as_sdt=0,5 | 4 | 2,019 |
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation | 80 | neurips | 0 | 0 | 2023-06-15 23:42:41.132000 | https://github.com/cwein3/jacobian-reg | 0 | Data-dependent sample complexity of deep neural networks via lipschitz augmentation | https://scholar.google.com/scholar?cluster=17001639970968112177&hl=en&as_sdt=0,5 | 2 | 2,019 |
Semi-supervisedly Co-embedding Attributed Networks | 27 | neurips | 0 | 0 | 2023-06-15 23:42:41.314000 | https://github.com/mengzaiqiao/SCAN | 30 | Semi-supervisedly co-embedding attributed networks | https://scholar.google.com/scholar?cluster=14232143209027006977&hl=en&as_sdt=0,29 | 3 | 2,019 |
Adaptive Auxiliary Task Weighting for Reinforcement Learning | 82 | neurips | 1 | 0 | 2023-06-15 23:42:41.510000 | https://github.com/Xingyu-Lin/auxiliary-tasks-rl | 20 | Adaptive auxiliary task weighting for reinforcement learning | https://scholar.google.com/scholar?cluster=6568043272475560239&hl=en&as_sdt=0,10 | 3 | 2,019 |
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders | 127 | neurips | 34 | 5 | 2023-06-15 23:42:41.692000 | https://github.com/emilemathieu/pvae | 114 | Continuous hierarchical representations with poincaré variational auto-encoders | https://scholar.google.com/scholar?cluster=4743584755071980119&hl=en&as_sdt=0,39 | 6 | 2,019 |
Training Image Estimators without Image Ground Truth | 18 | neurips | 2 | 0 | 2023-06-15 23:42:41.874000 | https://github.com/likesum/unsupimg | 12 | Training image estimators without image ground truth | https://scholar.google.com/scholar?cluster=8370564258628427561&hl=en&as_sdt=0,5 | 4 | 2,019 |
Minimizers of the Empirical Risk and Risk Monotonicity | 21 | neurips | 0 | 0 | 2023-06-15 23:42:42.057000 | https://github.com/tomviering/RiskMonotonicity | 1 | Minimizers of the empirical risk and risk monotonicity | https://scholar.google.com/scholar?cluster=13614749018190091572&hl=en&as_sdt=0,5 | 1 | 2,019 |
The Label Complexity of Active Learning from Observational Data | 8 | neurips | 0 | 0 | 2023-06-15 23:42:42.239000 | https://github.com/yyysbysb/al_obs_neurips19 | 0 | The label complexity of active learning from observational data | https://scholar.google.com/scholar?cluster=11282037010196502845&hl=en&as_sdt=0,5 | 1 | 2,019 |
Learning Fairness in Multi-Agent Systems | 43 | neurips | 9 | 0 | 2023-06-15 23:42:42.421000 | https://github.com/PKU-AI-Edge/FEN | 34 | Learning fairness in multi-agent systems | https://scholar.google.com/scholar?cluster=2510823275080690195&hl=en&as_sdt=0,6 | 2 | 2,019 |
On Robustness to Adversarial Examples and Polynomial Optimization | 32 | neurips | 0 | 0 | 2023-06-15 23:42:42.602000 | https://github.com/abhrodutta/advrobust | 0 | On robustness to adversarial examples and polynomial optimization | https://scholar.google.com/scholar?cluster=14449715261251259195&hl=en&as_sdt=0,39 | 1 | 2,019 |
In-Place Zero-Space Memory Protection for CNN | 18 | neurips | 2 | 0 | 2023-06-15 23:42:42.784000 | https://github.com/guanh01/wot | 2 | In-place zero-space memory protection for cnn | https://scholar.google.com/scholar?cluster=7089788483672559096&hl=en&as_sdt=0,15 | 2 | 2,019 |
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask | 327 | neurips | 28 | 2 | 2023-06-15 23:42:42.965000 | https://github.com/uber-research/deconstructing-lottery-tickets | 137 | Deconstructing lottery tickets: Zeros, signs, and the supermask | https://scholar.google.com/scholar?cluster=6213271169293396055&hl=en&as_sdt=0,36 | 7 | 2,019 |
Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes | 179 | neurips | 22 | 1 | 2023-06-15 23:42:43.147000 | https://github.com/cambridge-mlg/cnaps | 152 | Fast and flexible multi-task classification using conditional neural adaptive processes | https://scholar.google.com/scholar?cluster=6556255070381758438&hl=en&as_sdt=0,44 | 11 | 2,019 |
A Simple Baseline for Bayesian Uncertainty in Deep Learning | 601 | neurips | 73 | 9 | 2023-06-15 23:42:43.329000 | https://github.com/wjmaddox/swa_gaussian | 387 | A simple baseline for bayesian uncertainty in deep learning | https://scholar.google.com/scholar?cluster=4938182174332558509&hl=en&as_sdt=0,43 | 12 | 2,019 |
CPM-Nets: Cross Partial Multi-View Networks | 71 | neurips | 26 | 2 | 2023-06-15 23:42:43.517000 | https://github.com/hanmenghan/CPM_Nets | 72 | CPM-Nets: Cross partial multi-view networks | https://scholar.google.com/scholar?cluster=3047426886148116831&hl=en&as_sdt=0,33 | 3 | 2,019 |
Efficiently avoiding saddle points with zero order methods: No gradients required | 18 | neurips | 2 | 0 | 2023-06-15 23:42:43.699000 | https://github.com/lamflokas/zero-order | 3 | Efficiently avoiding saddle points with zero order methods: No gradients required | https://scholar.google.com/scholar?cluster=13601784096237697106&hl=en&as_sdt=0,33 | 2 | 2,019 |
Learning metrics for persistence-based summaries and applications for graph classification | 93 | neurips | 1 | 1 | 2023-06-15 23:42:43.881000 | https://github.com/topology474/WKPI | 11 | Learning metrics for persistence-based summaries and applications for graph classification | https://scholar.google.com/scholar?cluster=9051382955304665692&hl=en&as_sdt=0,33 | 1 | 2,019 |
PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph | 69 | neurips | 9 | 7 | 2023-06-15 23:42:44.063000 | https://github.com/yikang-li/PasteGAN | 51 | Pastegan: A semi-parametric method to generate image from scene graph | https://scholar.google.com/scholar?cluster=13530635324497263822&hl=en&as_sdt=0,5 | 1 | 2,019 |
Learning Local Search Heuristics for Boolean Satisfiability | 89 | neurips | 7 | 1 | 2023-06-15 23:42:44.245000 | https://github.com/emreyolcu/sat | 29 | Learning local search heuristics for boolean satisfiability | https://scholar.google.com/scholar?cluster=13065026334789781574&hl=en&as_sdt=0,38 | 3 | 2,019 |
Learning to Perform Local Rewriting for Combinatorial Optimization | 231 | neurips | 48 | 8 | 2023-06-15 23:42:44.430000 | https://github.com/facebookresearch/neural-rewriter | 138 | Learning to perform local rewriting for combinatorial optimization | https://scholar.google.com/scholar?cluster=13941022610350989164&hl=en&as_sdt=0,20 | 7 | 2,019 |
Learning Representations for Time Series Clustering | 129 | neurips | 21 | 8 | 2023-06-15 23:42:44.612000 | https://github.com/qianlima-lab/DTCR | 69 | Learning representations for time series clustering | https://scholar.google.com/scholar?cluster=8145184496367809324&hl=en&as_sdt=0,4 | 9 | 2,019 |
Joint-task Self-supervised Learning for Temporal Correspondence | 116 | neurips | 23 | 1 | 2023-06-15 23:42:44.795000 | https://github.com/Liusifei/UVC | 172 | Joint-task self-supervised learning for temporal correspondence | https://scholar.google.com/scholar?cluster=15162867613361199730&hl=en&as_sdt=0,31 | 13 | 2,019 |
On Distributed Averaging for Stochastic k-PCA | 8 | neurips | 0 | 0 | 2023-06-15 23:42:44.976000 | https://github.com/maheshakya/dist-averaging-k-pca | 2 | On distributed averaging for stochastic k-PCA | https://scholar.google.com/scholar?cluster=3460811999232599777&hl=en&as_sdt=0,5 | 3 | 2,019 |
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control | 73 | neurips | 13 | 9 | 2023-06-15 23:42:45.159000 | https://github.com/saizhang0218/VBC | 41 | Efficient communication in multi-agent reinforcement learning via variance based control | https://scholar.google.com/scholar?cluster=14090873804037155766&hl=en&as_sdt=0,33 | 4 | 2,019 |
A Bayesian Theory of Conformity in Collective Decision Making | 9 | neurips | 0 | 0 | 2023-06-15 23:42:45.341000 | https://github.com/kooosha/BayesianConformity | 1 | A Bayesian theory of conformity in collective decision making | https://scholar.google.com/scholar?cluster=7455154068754976194&hl=en&as_sdt=0,14 | 1 | 2,019 |
Poisson-Randomized Gamma Dynamical Systems | 21 | neurips | 2 | 0 | 2023-06-15 23:42:45.523000 | https://github.com/aschein/PRGDS | 7 | Poisson-randomized gamma dynamical systems | https://scholar.google.com/scholar?cluster=6917148610185425748&hl=en&as_sdt=0,5 | 2 | 2,019 |
Sequence Modeling with Unconstrained Generation Order | 18 | neurips | 4 | 4 | 2023-06-15 23:42:45.705000 | https://github.com/TIXFeniks/neurips2019_intrus | 15 | Sequence modeling with unconstrained generation order | https://scholar.google.com/scholar?cluster=11928975685128979284&hl=en&as_sdt=0,10 | 3 | 2,019 |
Online Continual Learning with Maximal Interfered Retrieval | 83 | neurips | 17 | 7 | 2023-06-15 23:42:45.886000 | https://github.com/optimass/Maximally_Interfered_Retrieval | 81 | Online class-incremental continual learning with adversarial shapley value | https://scholar.google.com/scholar?cluster=13286994926038359819&hl=en&as_sdt=0,36 | 8 | 2,019 |
Deep Generalized Method of Moments for Instrumental Variable Analysis | 96 | neurips | 6 | 0 | 2023-06-15 23:42:46.069000 | https://github.com/CausalML/DeepGMM | 30 | Deep generalized method of moments for instrumental variable analysis | https://scholar.google.com/scholar?cluster=2190218199983415707&hl=en&as_sdt=0,33 | 5 | 2,019 |
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders | 26 | neurips | 3 | 0 | 2023-06-15 23:42:46.251000 | https://github.com/tagas/vcae | 6 | Copulas as high-dimensional generative models: Vine copula autoencoders | https://scholar.google.com/scholar?cluster=7223084287803967462&hl=en&as_sdt=0,33 | 1 | 2,019 |
Implicit Semantic Data Augmentation for Deep Networks | 126 | neurips | 91 | 7 | 2023-06-15 23:42:46.434000 | https://github.com/blackfeather-wang/ISDA-for-Deep-Networks | 558 | Implicit semantic data augmentation for deep networks | https://scholar.google.com/scholar?cluster=7550212963296230236&hl=en&as_sdt=0,5 | 15 | 2,019 |
q-means: A quantum algorithm for unsupervised machine learning | 143 | neurips | 2 | 1 | 2023-06-15 23:42:46.616000 | https://github.com/JonasLandman/quantum_kmeans_NeurIPS_2019 | 6 | q-means: A quantum algorithm for unsupervised machine learning | https://scholar.google.com/scholar?cluster=6188393801436319062&hl=en&as_sdt=0,47 | 1 | 2,019 |
A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression | 14 | neurips | 3 | 0 | 2023-06-15 23:42:46.798000 | https://github.com/gerrili1996/DRLR_NIPS2019_exp | 12 | A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression | https://scholar.google.com/scholar?cluster=14059374198558269929&hl=en&as_sdt=0,13 | 2 | 2,019 |
Robust Attribution Regularization | 58 | neurips | 0 | 2 | 2023-06-15 23:42:46.981000 | https://github.com/jfc43/robust-attribution-regularization | 15 | Robust attribution regularization | https://scholar.google.com/scholar?cluster=9772102979248482022&hl=en&as_sdt=0,33 | 2 | 2,019 |
Kernel Instrumental Variable Regression | 122 | neurips | 1 | 0 | 2023-06-15 23:42:47.163000 | https://github.com/r4hu1-5in9h/KIV | 7 | Kernel instrumental variable regression | https://scholar.google.com/scholar?cluster=14048410024611042671&hl=en&as_sdt=0,33 | 1 | 2,019 |
Hindsight Credit Assignment | 63 | neurips | 1 | 0 | 2023-06-15 23:42:47.345000 | https://github.com/hca-neurips2019/hca | 8 | Hindsight credit assignment | https://scholar.google.com/scholar?cluster=4046462463580411762&hl=en&as_sdt=0,33 | 2 | 2,019 |
Zero-shot Learning via Simultaneous Generating and Learning | 45 | neurips | 1 | 0 | 2023-06-15 23:42:47.551000 | https://github.com/bogus2000/zero-shot_SGAL | 2 | Zero-shot learning via simultaneous generating and learning | https://scholar.google.com/scholar?cluster=4888611816499728878&hl=en&as_sdt=0,14 | 4 | 2,019 |
Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder | 34 | neurips | 1 | 1 | 2023-06-15 23:42:47.737000 | https://github.com/GuyLor/direct_vae | 14 | Direct Optimization through for Discrete Variational Auto-Encoder | https://scholar.google.com/scholar?cluster=9304709167594459468&hl=en&as_sdt=0,33 | 3 | 2,019 |
Ouroboros: On Accelerating Training of Transformer-Based Language Models | 5 | neurips | 1 | 1 | 2023-06-15 23:42:47.919000 | https://github.com/LaraQianYang/Ouroboros | 10 | Ouroboros: On accelerating training of transformer-based language models | https://scholar.google.com/scholar?cluster=5857133674460297105&hl=en&as_sdt=0,33 | 2 | 2,019 |
Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently | 2 | neurips | 0 | 0 | 2023-06-15 23:42:48.100000 | https://github.com/XiaoLiu-git/Push-Pull-feedback-for-NIPS2019 | 2 | Push-pull feedback implements hierarchical information retrieval efficiently | https://scholar.google.com/scholar?cluster=9412623594007001051&hl=en&as_sdt=0,31 | 1 | 2,019 |
Calibration tests in multi-class classification: A unifying framework | 70 | neurips | 4 | 0 | 2023-06-15 23:42:48.283000 | https://github.com/devmotion/CalibrationPaper | 15 | Calibration tests in multi-class classification: A unifying framework | https://scholar.google.com/scholar?cluster=3801848561463868777&hl=en&as_sdt=0,5 | 2 | 2,019 |
Globally Optimal Learning for Structured Elliptical Losses | 4 | neurips | 0 | 0 | 2023-06-15 23:42:48.465000 | https://github.com/yowald/elliptical-losses | 0 | Globally optimal learning for structured elliptical losses | https://scholar.google.com/scholar?cluster=13004269244782934257&hl=en&as_sdt=0,32 | 2 | 2,019 |
MixMatch: A Holistic Approach to Semi-Supervised Learning | 2,316 | neurips | 162 | 2 | 2023-06-15 23:42:48.648000 | https://github.com/google-research/mixmatch | 1,107 | Mixmatch: A holistic approach to semi-supervised learning | https://scholar.google.com/scholar?cluster=8843329865264835946&hl=en&as_sdt=0,29 | 26 | 2,019 |
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks | 69 | neurips | 3 | 0 | 2023-06-15 23:42:48.830000 | https://github.com/ColinQiyangLi/LConvNet | 32 | Preventing gradient attenuation in lipschitz constrained convolutional networks | https://scholar.google.com/scholar?cluster=16988033014976745098&hl=en&as_sdt=0,33 | 8 | 2,019 |
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder | 60 | neurips | 10 | 1 | 2023-06-15 23:42:49.012000 | https://github.com/kingfengji/DeepConfuse | 15 | Learning to confuse: generating training time adversarial data with auto-encoder | https://scholar.google.com/scholar?cluster=8039257054825778707&hl=en&as_sdt=0,33 | 2 | 2,019 |
Attentive State-Space Modeling of Disease Progression | 78 | neurips | 1 | 1 | 2023-06-15 23:42:49.194000 | https://github.com/ahmedmalaa/attentive-state-space-models | 5 | Attentive state-space modeling of disease progression | https://scholar.google.com/scholar?cluster=16630755121870037288&hl=en&as_sdt=0,33 | 1 | 2,019 |
On two ways to use determinantal point processes for Monte Carlo integration | 19 | neurips | 47 | 3 | 2023-06-15 23:42:49.376000 | https://github.com/guilgautier/DPPy | 204 | On two ways to use determinantal point processes for Monte Carlo integration | https://scholar.google.com/scholar?cluster=12801077756584329210&hl=en&as_sdt=0,44 | 16 | 2,019 |
Controllable Text-to-Image Generation | 248 | neurips | 35 | 9 | 2023-06-15 23:42:49.558000 | https://github.com/mrlibw/ControlGAN | 154 | Controllable text-to-image generation | https://scholar.google.com/scholar?cluster=18438617826827121407&hl=en&as_sdt=0,3 | 5 | 2,019 |
Exploring Algorithmic Fairness in Robust Graph Covering Problems | 44 | neurips | 0 | 0 | 2023-06-15 23:42:49.740000 | https://github.com/Aida-Rahmattalabi/Fair-and-Robust-Graph-Covering-Problem | 0 | Exploring algorithmic fairness in robust graph covering problems | https://scholar.google.com/scholar?cluster=12434116312128115468&hl=en&as_sdt=0,21 | 2 | 2,019 |
Reducing the variance in online optimization by transporting past gradients | 17 | neurips | 4 | 0 | 2023-06-15 23:42:49.922000 | https://github.com/seba-1511/igt.pth | 19 | Reducing the variance in online optimization by transporting past gradients | https://scholar.google.com/scholar?cluster=11851078121224648167&hl=en&as_sdt=0,22 | 2 | 2,019 |
Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces | 11 | neurips | 0 | 0 | 2023-06-15 23:42:50.104000 | https://github.com/BenyaminHaghi/DRNN-NeurIPS2019 | 3 | Deep multi-state dynamic recurrent neural networks operating on wavelet based neural features for robust brain machine interfaces | https://scholar.google.com/scholar?cluster=3157207408817715516&hl=en&as_sdt=0,5 | 2 | 2,019 |
Graph Normalizing Flows | 120 | neurips | 9 | 1 | 2023-06-15 23:42:50.289000 | https://github.com/jliu/graph-normalizing-flows | 51 | Graph normalizing flows | https://scholar.google.com/scholar?cluster=6217003823506794566&hl=en&as_sdt=0,44 | 3 | 2,019 |
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction | 27 | neurips | 3 | 2 | 2023-06-15 23:42:50.471000 | https://github.com/tinyRattar/CSMRI_0325 | 31 | Cascaded dilated dense network with two-step data consistency for MRI reconstruction | https://scholar.google.com/scholar?cluster=8948167935740989245&hl=en&as_sdt=0,5 | 1 | 2,019 |
Likelihood Ratios for Out-of-Distribution Detection | 520 | neurips | 7,320 | 1,025 | 2023-06-15 23:42:50.653000 | https://github.com/google-research/google-research | 29,776 | Likelihood ratios for out-of-distribution detection | https://scholar.google.com/scholar?cluster=8139743879647518819&hl=en&as_sdt=0,5 | 727 | 2,019 |
Root Mean Square Layer Normalization | 61 | neurips | 8 | 1 | 2023-06-15 23:42:50.836000 | https://github.com/bzhangGo/rmsnorm | 85 | Root mean square layer normalization | https://scholar.google.com/scholar?cluster=14510401956062153654&hl=en&as_sdt=0,44 | 4 | 2,019 |
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs | 230 | neurips | 29 | 1 | 2023-06-15 23:42:51.018000 | https://github.com/malllabiisc/HyperGCN | 149 | Hypergcn: A new method for training graph convolutional networks on hypergraphs | https://scholar.google.com/scholar?cluster=15969550418562746882&hl=en&as_sdt=0,5 | 4 | 2,019 |
Asymptotics for Sketching in Least Squares Regression | 46 | neurips | 0 | 0 | 2023-06-15 23:42:51.200000 | https://github.com/liusf15/Sketching-lr | 6 | Asymptotics for sketching in least squares regression | https://scholar.google.com/scholar?cluster=15974284881212026829&hl=en&as_sdt=0,5 | 2 | 2,019 |
TAB-VCR: Tags and Attributes based VCR Baselines | 18 | neurips | 8 | 1 | 2023-06-15 23:42:51.382000 | https://github.com/Deanplayerljx/tab-vcr | 19 | TAB-VCR: tags and attributes based VCR baselines | https://scholar.google.com/scholar?cluster=9340006550107070175&hl=en&as_sdt=0,43 | 3 | 2,019 |
Assessing Social and Intersectional Biases in Contextualized Word Representations | 157 | neurips | 2 | 0 | 2023-06-15 23:42:51.564000 | https://github.com/tanyichern/social-biases-contextualized | 4 | Assessing social and intersectional biases in contextualized word representations | https://scholar.google.com/scholar?cluster=434026761341591486&hl=en&as_sdt=0,38 | 1 | 2,019 |
Likelihood-Free Overcomplete ICA and Applications In Causal Discovery | 7 | neurips | 0 | 0 | 2023-06-15 23:42:51.746000 | https://github.com/dingchenwei/Likelihood-free_OICA | 9 | Likelihood-free overcomplete ICA and applications in causal discovery | https://scholar.google.com/scholar?cluster=11860404397315313047&hl=en&as_sdt=0,33 | 1 | 2,019 |
MaCow: Masked Convolutional Generative Flow | 48 | neurips | 4 | 1 | 2023-06-15 23:42:51.928000 | https://github.com/XuezheMax/macow | 58 | Macow: Masked convolutional generative flow | https://scholar.google.com/scholar?cluster=149053927575210131&hl=en&as_sdt=0,31 | 4 | 2,019 |
Batched Multi-armed Bandits Problem | 101 | neurips | 1 | 0 | 2023-06-15 23:42:52.110000 | https://github.com/Mathegineer/batched-bandit | 3 | Batched multi-armed bandits problem | https://scholar.google.com/scholar?cluster=1369955008472544839&hl=en&as_sdt=0,5 | 1 | 2,019 |
Causal Regularization | 33 | neurips | 1 | 0 | 2023-06-15 23:42:52.293000 | https://github.com/janzing/janzing.github.io | 4 | Causal regularization | https://scholar.google.com/scholar?cluster=6604566561905490847&hl=en&as_sdt=0,10 | 0 | 2,019 |
Augmented Neural ODEs | 445 | neurips | 84 | 10 | 2023-06-15 23:42:52.474000 | https://github.com/EmilienDupont/augmented-neural-odes | 487 | Augmented neural odes | https://scholar.google.com/scholar?cluster=2463018982232972510&hl=en&as_sdt=0,5 | 19 | 2,019 |
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