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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
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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
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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
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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
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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
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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
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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
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neurips
| 7
| 3
|
2023-06-15 23:42:37.120000
|
https://github.com/aravindr93/imaml_dev
| 42
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Meta-learning with implicit gradients
|
https://scholar.google.com/scholar?cluster=13369476722285367510&hl=en&as_sdt=0,5
| 6
| 2,019
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Differentially Private Markov Chain Monte Carlo
| 20
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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
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Universal Boosting Variational Inference
| 25
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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
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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
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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
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Learning Stable Deep Dynamics Models
| 143
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neurips
| 10
| 2
|
2023-06-15 23:42:38.397000
|
https://github.com/locuslab/stable_dynamics
| 25
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Learning stable deep dynamics models
|
https://scholar.google.com/scholar?cluster=15884383241607994844&hl=en&as_sdt=0,26
| 2
| 2,019
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Beyond the Single Neuron Convex Barrier for Neural Network Certification
| 140
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neurips
| 99
| 12
|
2023-06-15 23:42:38.579000
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https://github.com/eth-sri/eran
| 284
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Beyond the single neuron convex barrier for neural network certification
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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
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neurips
| 33
| 2
|
2023-06-15 23:42:38.761000
|
https://github.com/iffsid/mmvae
| 142
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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
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neurips
| 12
| 4
|
2023-06-15 23:42:38.944000
|
https://github.com/google-research/clevr_robot_env
| 118
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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
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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
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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
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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
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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
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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
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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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