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How to Characterize The Landscape of Overparameterized Convolutional Neural Networks
| 9 |
neurips
| 0 | 0 |
2023-06-16 15:10:13.489000
|
https://github.com/wmyw96/convex-cnn-tf
| 0 |
How to characterize the landscape of overparameterized convolutional neural networks
|
https://scholar.google.com/scholar?cluster=16949672964324049904&hl=en&as_sdt=0,5
| 1 | 2,020 |
Adaptive Discretization for Model-Based Reinforcement Learning
| 19 |
neurips
| 2 | 0 |
2023-06-16 15:10:13.680000
|
https://github.com/seanrsinclair/AdaptiveQLearning
| 1 |
Adaptive discretization for model-based reinforcement learning
|
https://scholar.google.com/scholar?cluster=16783221082226799&hl=en&as_sdt=0,46
| 1 | 2,020 |
CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching
| 39 |
neurips
| 13 | 4 |
2023-06-16 15:10:13.872000
|
https://github.com/binaryai/CodeCMR
| 43 |
Codecmr: Cross-modal retrieval for function-level binary source code matching
|
https://scholar.google.com/scholar?cluster=8935328746274345549&hl=en&as_sdt=0,19
| 4 | 2,020 |
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks
| 30 |
neurips
| 2 | 1 |
2023-06-16 15:10:14.065000
|
https://github.com/skypea/DAG_No_Fear
| 10 |
DAGs with No Fears: A closer look at continuous optimization for learning Bayesian networks
|
https://scholar.google.com/scholar?cluster=1019446956575519869&hl=en&as_sdt=0,50
| 3 | 2,020 |
Teaching a GAN What Not to Learn
| 14 |
neurips
| 6 | 1 |
2023-06-16 15:10:14.257000
|
https://github.com/DarthSid95/RumiGANs
| 29 |
Teaching a gan what not to learn
|
https://scholar.google.com/scholar?cluster=5006743411241941329&hl=en&as_sdt=0,3
| 2 | 2,020 |
Rethinking Learnable Tree Filter for Generic Feature Transform
| 13 |
neurips
| 9 | 7 |
2023-06-16 15:10:14.459000
|
https://github.com/StevenGrove/LearnableTreeFilterV2
| 89 |
Rethinking learnable tree filter for generic feature transform
|
https://scholar.google.com/scholar?cluster=18019390806247170102&hl=en&as_sdt=0,33
| 2 | 2,020 |
Self-Supervised Relational Reasoning for Representation Learning
| 43 |
neurips
| 24 | 0 |
2023-06-16 15:10:14.652000
|
https://github.com/mpatacchiola/self-supervised-relational-reasoning
| 136 |
Self-supervised relational reasoning for representation learning
|
https://scholar.google.com/scholar?cluster=4065282984130236161&hl=en&as_sdt=0,44
| 7 | 2,020 |
Sufficient dimension reduction for classification using principal optimal transport direction
| 12 |
neurips
| 2 | 0 |
2023-06-16 15:10:14.844000
|
https://github.com/ChengzijunAixiaoli/POTD
| 4 |
Sufficient dimension reduction for classification using principal optimal transport direction
|
https://scholar.google.com/scholar?cluster=9453699128109678882&hl=en&as_sdt=0,5
| 1 | 2,020 |
Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine
| 11 |
neurips
| 2 | 0 |
2023-06-16 15:10:15.039000
|
https://github.com/gerrili1996/Incremental_DRSVM
| 0 |
Fast epigraphical projection-based incremental algorithms for Wasserstein distributionally robust support vector machine
|
https://scholar.google.com/scholar?cluster=17557069801985892953&hl=en&as_sdt=0,33
| 2 | 2,020 |
Adaptive Reduced Rank Regression
| 14 |
neurips
| 5 | 0 |
2023-06-16 15:10:15.255000
|
https://github.com/Qiong-WU/ARRR_code
| 29 |
Adaptive reduced rank regression
|
https://scholar.google.com/scholar?cluster=833219182915456157&hl=en&as_sdt=0,48
| 2 | 2,020 |
Learning Loss for Test-Time Augmentation
| 50 |
neurips
| 2 | 2 |
2023-06-16 15:10:15.466000
|
https://github.com/bayesgroup/gps-augment
| 35 |
Learning loss for test-time augmentation
|
https://scholar.google.com/scholar?cluster=11423734549303606224&hl=en&as_sdt=0,30
| 12 | 2,020 |
Balanced Meta-Softmax for Long-Tailed Visual Recognition
| 238 |
neurips
| 10 | 0 |
2023-06-16 15:10:15.661000
|
https://github.com/jiawei-ren/BalancedMetaSoftmax
| 66 |
Balanced meta-softmax for long-tailed visual recognition
|
https://scholar.google.com/scholar?cluster=6313928950899865573&hl=en&as_sdt=0,5
| 6 | 2,020 |
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
| 78 |
neurips
| 2 | 0 |
2023-06-16 15:10:15.854000
|
https://github.com/ElisevanderPol/mdp-homomorphic-networks
| 22 |
Mdp homomorphic networks: Group symmetries in reinforcement learning
|
https://scholar.google.com/scholar?cluster=3290101781386627154&hl=en&as_sdt=0,5
| 2 | 2,020 |
Object Goal Navigation using Goal-Oriented Semantic Exploration
| 259 |
neurips
| 44 | 6 |
2023-06-16 15:10:16.047000
|
https://github.com/devendrachaplot/Object-Goal-Navigation
| 169 |
Object goal navigation using goal-oriented semantic exploration
|
https://scholar.google.com/scholar?cluster=2452364222221336490&hl=en&as_sdt=0,5
| 5 | 2,020 |
Efficient semidefinite-programming-based inference for binary and multi-class MRFs
| 3 |
neurips
| 0 | 0 |
2023-06-16 15:10:16.241000
|
https://github.com/locuslab/sdp_mrf
| 3 |
Efficient semidefinite-programming-based inference for binary and multi-class MRFs
|
https://scholar.google.com/scholar?cluster=795899549396489666&hl=en&as_sdt=0,33
| 6 | 2,020 |
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
| 123 |
neurips
| 15 | 9 |
2023-06-16 15:10:16.467000
|
https://github.com/laiguokun/Funnel-Transformer
| 197 |
Funnel-transformer: Filtering out sequential redundancy for efficient language processing
|
https://scholar.google.com/scholar?cluster=13758108828249747636&hl=en&as_sdt=0,33
| 11 | 2,020 |
Semantic Visual Navigation by Watching YouTube Videos
| 48 |
neurips
| 0 | 19 |
2023-06-16 15:10:16.660000
|
https://github.com/MatthewChang/video-dqn
| 22 |
Semantic visual navigation by watching youtube videos
|
https://scholar.google.com/scholar?cluster=5339143065575935853&hl=en&as_sdt=0,39
| 2 | 2,020 |
Learning Differential Equations that are Easy to Solve
| 76 |
neurips
| 31 | 4 |
2023-06-16 15:10:16.853000
|
https://github.com/jacobjinkelly/easy-neural-ode
| 245 |
Learning differential equations that are easy to solve
|
https://scholar.google.com/scholar?cluster=17384297955183349294&hl=en&as_sdt=0,44
| 10 | 2,020 |
Influence-Augmented Online Planning for Complex Environments
| 7 |
neurips
| 1 | 0 |
2023-06-16 15:10:17.045000
|
https://github.com/INFLUENCEorg/IAOP
| 3 |
Influence-augmented online planning for complex environments
|
https://scholar.google.com/scholar?cluster=11045895327185763569&hl=en&as_sdt=0,5
| 3 | 2,020 |
Probabilistic Time Series Forecasting with Shape and Temporal Diversity
| 21 |
neurips
| 16 | 2 |
2023-06-16 15:10:17.237000
|
https://github.com/vincent-leguen/STRIPE
| 74 |
Probabilistic time series forecasting with shape and temporal diversity
|
https://scholar.google.com/scholar?cluster=1337249375985233521&hl=en&as_sdt=0,30
| 3 | 2,020 |
Continual Deep Learning by Functional Regularisation of Memorable Past
| 82 |
neurips
| 4 | 1 |
2023-06-16 15:10:17.443000
|
https://github.com/team-approx-bayes/fromp
| 37 |
Continual deep learning by functional regularisation of memorable past
|
https://scholar.google.com/scholar?cluster=10115135321591353527&hl=en&as_sdt=0,33
| 2 | 2,020 |
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning
| 162 |
neurips
| 23 | 1 |
2023-06-16 15:10:17.637000
|
https://github.com/snap-stanford/distance-encoding
| 173 |
Distance encoding: Design provably more powerful neural networks for graph representation learning
|
https://scholar.google.com/scholar?cluster=6342884862045270520&hl=en&as_sdt=0,5
| 8 | 2,020 |
Fast Fourier Convolution
| 137 |
neurips
| 28 | 8 |
2023-06-16 15:10:17.829000
|
https://github.com/pkumivision/FFC
| 237 |
Fast fourier convolution
|
https://scholar.google.com/scholar?cluster=2160547042943986472&hl=en&as_sdt=0,33
| 4 | 2,020 |
Learning Structured Distributions From Untrusted Batches: Faster and Simpler
| 12 |
neurips
| 0 | 0 |
2023-06-16 15:10:18.022000
|
https://github.com/secanth/federated
| 1 |
Learning structured distributions from untrusted batches: Faster and simpler
|
https://scholar.google.com/scholar?cluster=8991328284889449701&hl=en&as_sdt=0,33
| 1 | 2,020 |
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks
| 49 |
neurips
| 7 | 0 |
2023-06-16 15:10:18.215000
|
https://github.com/ermongroup/ODS
| 50 |
Diversity can be transferred: Output diversification for white-and black-box attacks
|
https://scholar.google.com/scholar?cluster=13509573931669660487&hl=en&as_sdt=0,47
| 8 | 2,020 |
Efficient Low Rank Gaussian Variational Inference for Neural Networks
| 19 |
neurips
| 1 | 1 |
2023-06-16 15:10:18.408000
|
https://github.com/marctom/elrgvi
| 2 |
Efficient low rank gaussian variational inference for neural networks
|
https://scholar.google.com/scholar?cluster=9190851527291082244&hl=en&as_sdt=0,5
| 2 | 2,020 |
Probabilistic Circuits for Variational Inference in Discrete Graphical Models
| 19 |
neurips
| 0 | 0 |
2023-06-16 15:10:18.600000
|
https://github.com/AndyShih12/SPN_Variational_Inference
| 14 |
Probabilistic circuits for variational inference in discrete graphical models
|
https://scholar.google.com/scholar?cluster=8548433346916922000&hl=en&as_sdt=0,32
| 2 | 2,020 |
Labelling unlabelled videos from scratch with multi-modal self-supervision
| 108 |
neurips
| 14 | 4 |
2023-06-16 15:10:18.792000
|
https://github.com/facebookresearch/selavi
| 108 |
Labelling unlabelled videos from scratch with multi-modal self-supervision
|
https://scholar.google.com/scholar?cluster=6374132588879486685&hl=en&as_sdt=0,5
| 12 | 2,020 |
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
| 405 |
neurips
| 34 | 6 |
2023-06-16 15:10:18.984000
|
https://github.com/izmailovpavel/understandingbdl
| 215 |
Bayesian deep learning and a probabilistic perspective of generalization
|
https://scholar.google.com/scholar?cluster=13252502369933124881&hl=en&as_sdt=0,5
| 6 | 2,020 |
Unsupervised Learning of Object Landmarks via Self-Training Correspondence
| 10 |
neurips
| 7 | 0 |
2023-06-16 15:10:19.176000
|
https://github.com/malldimi1/UnsupervisedLandmarks
| 22 |
Unsupervised learning of object landmarks via self-training correspondence
|
https://scholar.google.com/scholar?cluster=5849709549192485830&hl=en&as_sdt=0,10
| 1 | 2,020 |
Generative View Synthesis: From Single-view Semantics to Novel-view Images
| 13 |
neurips
| 1 | 0 |
2023-06-16 15:10:19.382000
|
https://github.com/tedyhabtegebrial/gvsnet
| 20 |
Generative view synthesis: From single-view semantics to novel-view images
|
https://scholar.google.com/scholar?cluster=6878036878351382558&hl=en&as_sdt=0,5
| 5 | 2,020 |
Deep Variational Instance Segmentation
| 8 |
neurips
| 4 | 5 |
2023-06-16 15:10:19.576000
|
https://github.com/jia2lin3yuan1/2020-instanceSeg
| 25 |
Deep variational instance segmentation
|
https://scholar.google.com/scholar?cluster=16407992024714041715&hl=en&as_sdt=0,5
| 4 | 2,020 |
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence
| 25 |
neurips
| 11 | 4 |
2023-06-16 15:10:19.769000
|
https://github.com/liuf1990/Implicit_Dense_Correspondence
| 55 |
Learning implicit functions for topology-varying dense 3d shape correspondence
|
https://scholar.google.com/scholar?cluster=13134506600576574791&hl=en&as_sdt=0,10
| 9 | 2,020 |
Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems
| 12 |
neurips
| 1 | 0 |
2023-06-16 15:10:19.961000
|
https://github.com/flowersteam/holmes
| 5 |
Hierarchically organized latent modules for exploratory search in morphogenetic systems
|
https://scholar.google.com/scholar?cluster=8455312043752460622&hl=en&as_sdt=0,5
| 1 | 2,020 |
Probabilistic Orientation Estimation with Matrix Fisher Distributions
| 28 |
neurips
| 2 | 1 |
2023-06-16 15:10:20.152000
|
https://github.com/Davmo049/Public_prob_orientation_estimation_with_matrix_fisher_distributions
| 20 |
Probabilistic orientation estimation with matrix fisher distributions
|
https://scholar.google.com/scholar?cluster=13738889246738372199&hl=en&as_sdt=0,36
| 3 | 2,020 |
Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons
| 3 |
neurips
| 0 | 0 |
2023-06-16 15:10:20.345000
|
https://github.com/Pehlevan-Group/BalancedEIMinimax
| 3 |
Minimax dynamics of optimally balanced spiking networks of excitatory and inhibitory neurons
|
https://scholar.google.com/scholar?cluster=13427818061137891735&hl=en&as_sdt=0,5
| 4 | 2,020 |
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
| 125 |
neurips
| 4 | 1 |
2023-06-16 15:10:20.537000
|
https://github.com/Kaixiong-Zhou/DGN
| 32 |
Towards deeper graph neural networks with differentiable group normalization
|
https://scholar.google.com/scholar?cluster=15936617451529189150&hl=en&as_sdt=0,34
| 2 | 2,020 |
Stochastic Optimization for Performative Prediction
| 66 |
neurips
| 4 | 1 |
2023-06-16 15:10:20.729000
|
https://github.com/zykls/performative-prediction
| 20 |
Stochastic optimization for performative prediction
|
https://scholar.google.com/scholar?cluster=17793048602767737159&hl=en&as_sdt=0,5
| 3 | 2,020 |
Domain Adaptation as a Problem of Inference on Graphical Models
| 44 |
neurips
| 3 | 0 |
2023-06-16 15:10:20.921000
|
https://github.com/mgong2/DA_Infer
| 26 |
Domain adaptation as a problem of inference on graphical models
|
https://scholar.google.com/scholar?cluster=15196795471254372547&hl=en&as_sdt=0,44
| 2 | 2,020 |
HOI Analysis: Integrating and Decomposing Human-Object Interaction
| 58 |
neurips
| 46 | 2 |
2023-06-16 15:10:21.114000
|
https://github.com/DirtyHarryLYL/HAKE-Action-Torch
| 201 |
Hoi analysis: Integrating and decomposing human-object interaction
|
https://scholar.google.com/scholar?cluster=1869809068174176654&hl=en&as_sdt=0,45
| 11 | 2,020 |
Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering
| 11 |
neurips
| 1 | 0 |
2023-06-16 15:10:21.305000
|
https://github.com/MengLiuPurdue/SLQ
| 2 |
Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering
|
https://scholar.googleusercontent.com/scholar?q=cache:qcklNVm80uwJ:scholar.google.com/+Strongly+local+p-norm-cut+algorithms+for+semi-supervised+learning+and+local+graph+clustering&hl=en&as_sdt=0,33
| 3 | 2,020 |
Deep Direct Likelihood Knockoffs
| 16 |
neurips
| 4 | 0 |
2023-06-16 15:10:21.497000
|
https://github.com/rajesh-lab/ddlk
| 7 |
Deep direct likelihood knockoffs
|
https://scholar.google.com/scholar?cluster=6129032431811553962&hl=en&as_sdt=0,33
| 5 | 2,020 |
Meta-Neighborhoods
| 10 |
neurips
| 3 | 0 |
2023-06-16 15:10:21.688000
|
https://github.com/lupalab/Meta-Neighborhoods
| 7 |
Meta-neighborhoods
|
https://scholar.google.com/scholar?cluster=2219636310662669974&hl=en&as_sdt=0,1
| 2 | 2,020 |
A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons
| 8 |
neurips
| 1 | 0 |
2023-06-16 15:10:21.880000
|
https://github.com/gmahuas/2stepGLM
| 1 |
A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons
|
https://scholar.google.com/scholar?cluster=14549343330955673745&hl=en&as_sdt=0,14
| 1 | 2,020 |
Feature Importance Ranking for Deep Learning
| 66 |
neurips
| 14 | 2 |
2023-06-16 15:10:22.072000
|
https://github.com/maksym33/FeatureImportanceDL
| 31 |
Feature importance ranking for deep learning
|
https://scholar.google.com/scholar?cluster=10291349468278084866&hl=en&as_sdt=0,14
| 3 | 2,020 |
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks
| 40 |
neurips
| 1 | 0 |
2023-06-16 15:10:22.265000
|
https://github.com/umutsimsekli/Hausdorff-Dimension-and-Generalization
| 2 |
Hausdorff dimension, heavy tails, and generalization in neural networks
|
https://scholar.google.com/scholar?cluster=8886979563776893274&hl=en&as_sdt=0,5
| 3 | 2,020 |
Learning Physical Constraints with Neural Projections
| 24 |
neurips
| 2 | 1 |
2023-06-16 15:10:22.482000
|
https://github.com/y-sq/neural_proj
| 15 |
Learning physical constraints with neural projections
|
https://scholar.google.com/scholar?cluster=1914156028148083261&hl=en&as_sdt=0,33
| 2 | 2,020 |
Robust Optimization for Fairness with Noisy Protected Groups
| 85 |
neurips
| 2 | 1 |
2023-06-16 15:10:22.675000
|
https://github.com/wenshuoguo/robust-fairness-code
| 6 |
Robust optimization for fairness with noisy protected groups
|
https://scholar.google.com/scholar?cluster=5111841011798470081&hl=en&as_sdt=0,5
| 1 | 2,020 |
Noise-Contrastive Estimation for Multivariate Point Processes
| 14 |
neurips
| 2 | 0 |
2023-06-16 15:10:22.867000
|
https://github.com/HMEIatJHU/nce-mpp
| 15 |
Noise-contrastive estimation for multivariate point processes
|
https://scholar.google.com/scholar?cluster=10618761970260910492&hl=en&as_sdt=0,5
| 3 | 2,020 |
Multiscale Deep Equilibrium Models
| 139 |
neurips
| 29 | 0 |
2023-06-16 15:10:23.060000
|
https://github.com/locuslab/mdeq
| 222 |
Multiscale deep equilibrium models
|
https://scholar.google.com/scholar?cluster=9858453803735938369&hl=en&as_sdt=0,5
| 13 | 2,020 |
Sparse Graphical Memory for Robust Planning
| 41 |
neurips
| 8 | 1 |
2023-06-16 15:10:23.265000
|
https://github.com/scottemmons/sgm
| 28 |
Sparse graphical memory for robust planning
|
https://scholar.google.com/scholar?cluster=14782939310889640294&hl=en&as_sdt=0,36
| 6 | 2,020 |
Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction
| 13 |
neurips
| 1 | 0 |
2023-06-16 15:10:23.461000
|
https://github.com/otiliastr/brain_task_effect
| 6 |
Modeling task effects on meaning representation in the brain via zero-shot meg prediction
|
https://scholar.google.com/scholar?cluster=342859305245260360&hl=en&as_sdt=0,23
| 3 | 2,020 |
Robust Quantization: One Model to Rule Them All
| 51 |
neurips
| 7 | 5 |
2023-06-16 15:10:23.653000
|
https://github.com/moranshkolnik/RobustQuantization
| 32 |
Robust quantization: One model to rule them all
|
https://scholar.google.com/scholar?cluster=1861034670227893783&hl=en&as_sdt=0,5
| 6 | 2,020 |
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
| 68 |
neurips
| 23 | 2 |
2023-06-16 15:10:23.846000
|
https://github.com/deepmind/jax_verify
| 126 |
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
|
https://scholar.google.com/scholar?cluster=415562155937952325&hl=en&as_sdt=0,43
| 8 | 2,020 |
Federated Accelerated Stochastic Gradient Descent
| 108 |
neurips
| 1 | 0 |
2023-06-16 15:10:24.038000
|
https://github.com/hongliny/FedAc-NeurIPS20
| 12 |
Federated accelerated stochastic gradient descent
|
https://scholar.google.com/scholar?cluster=17827059715585826187&hl=en&as_sdt=0,44
| 2 | 2,020 |
An analytic theory of shallow networks dynamics for hinge loss classification
| 13 |
neurips
| 0 | 0 |
2023-06-16 15:10:24.230000
|
https://github.com/phiandark/DynHingeLoss
| 0 |
An analytic theory of shallow networks dynamics for hinge loss classification
|
https://scholar.google.com/scholar?cluster=9155304244259608841&hl=en&as_sdt=0,41
| 1 | 2,020 |
Learning to Orient Surfaces by Self-supervised Spherical CNNs
| 27 |
neurips
| 3 | 1 |
2023-06-16 15:10:24.422000
|
https://github.com/CVLAB-Unibo/compass
| 15 |
Learning to orient surfaces by self-supervised spherical cnns
|
https://scholar.google.com/scholar?cluster=13771145081900249763&hl=en&as_sdt=0,24
| 9 | 2,020 |
Parabolic Approximation Line Search for DNNs
| 12 |
neurips
| 3 | 1 |
2023-06-16 15:10:24.615000
|
https://github.com/cogsys-tuebingen/PAL
| 20 |
Parabolic approximation line search for dnns
|
https://scholar.google.com/scholar?cluster=15049615666059175813&hl=en&as_sdt=0,11
| 8 | 2,020 |
Generative causal explanations of black-box classifiers
| 49 |
neurips
| 10 | 0 |
2023-06-16 15:10:24.808000
|
https://github.com/siplab-gt/generative-causal-explanations
| 25 |
Generative causal explanations of black-box classifiers
|
https://scholar.google.com/scholar?cluster=11533502889457597902&hl=en&as_sdt=0,34
| 5 | 2,020 |
Sub-sampling for Efficient Non-Parametric Bandit Exploration
| 13 |
neurips
| 3 | 0 |
2023-06-16 15:10:25.002000
|
https://github.com/DBaudry/Sub-Sampling-Dueling-Algorithms-Neurips20
| 10 |
Sub-sampling for efficient non-parametric bandit exploration
|
https://scholar.google.com/scholar?cluster=15996804451950962772&hl=en&as_sdt=0,10
| 1 | 2,020 |
Learning under Model Misspecification: Applications to Variational and Ensemble methods
| 52 |
neurips
| 2 | 0 |
2023-06-16 15:10:25.195000
|
https://github.com/PGM-Lab/PAC2BAYES
| 9 |
Learning under model misspecification: Applications to variational and ensemble methods
|
https://scholar.google.com/scholar?cluster=12176489635076115022&hl=en&as_sdt=0,33
| 7 | 2,020 |
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
| 71 |
neurips
| 13 | 0 |
2023-06-16 15:10:25.386000
|
https://github.com/zjysteven/DVERGE
| 54 |
DVERGE: diversifying vulnerabilities for enhanced robust generation of ensembles
|
https://scholar.google.com/scholar?cluster=15783762290980425990&hl=en&as_sdt=0,5
| 1 | 2,020 |
Latent World Models For Intrinsically Motivated Exploration
| 19 |
neurips
| 2 | 0 |
2023-06-16 15:10:25.579000
|
https://github.com/htdt/lwm
| 18 |
Latent world models for intrinsically motivated exploration
|
https://scholar.google.com/scholar?cluster=5814046916674904026&hl=en&as_sdt=0,22
| 3 | 2,020 |
Training Generative Adversarial Networks by Solving Ordinary Differential Equations
| 25 |
neurips
| 2,436 | 170 |
2023-06-16 15:10:25.771000
|
https://github.com/deepmind/deepmind-research
| 11,902 |
Training generative adversarial networks by solving ordinary differential equations
|
https://scholar.google.com/scholar?cluster=5086997410208993615&hl=en&as_sdt=0,5
| 336 | 2,020 |
Learning of Discrete Graphical Models with Neural Networks
| 6 |
neurips
| 0 | 0 |
2023-06-16 15:10:25.963000
|
https://github.com/lanl-ansi/NeurISE
| 0 |
Learning of discrete graphical models with neural networks
|
https://scholar.google.com/scholar?cluster=17603472038483944187&hl=en&as_sdt=0,23
| 5 | 2,020 |
RepPoints v2: Verification Meets Regression for Object Detection
| 86 |
neurips
| 49 | 14 |
2023-06-16 15:10:26.154000
|
https://github.com/Scalsol/RepPointsV2
| 294 |
Reppoints v2: Verification meets regression for object detection
|
https://scholar.google.com/scholar?cluster=14843700105251392523&hl=en&as_sdt=0,47
| 10 | 2,020 |
Unfolding the Alternating Optimization for Blind Super Resolution
| 150 |
neurips
| 39 | 6 |
2023-06-16 15:10:26.346000
|
https://github.com/greatlog/DAN
| 204 |
Unfolding the alternating optimization for blind super resolution
|
https://scholar.google.com/scholar?cluster=16834542650773066132&hl=en&as_sdt=0,10
| 5 | 2,020 |
Entrywise convergence of iterative methods for eigenproblems
| 2 |
neurips
| 0 | 0 |
2023-06-16 15:10:26.539000
|
https://github.com/VHarisop/entrywise-convergence
| 0 |
Entrywise convergence of iterative methods for eigenproblems
|
https://scholar.google.com/scholar?cluster=4848039311509999194&hl=en&as_sdt=0,5
| 3 | 2,020 |
Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views
| 35 |
neurips
| 5 | 1 |
2023-06-16 15:10:26.731000
|
https://github.com/NanboLi/MulMON
| 16 |
Learning object-centric representations of multi-object scenes from multiple views
|
https://scholar.google.com/scholar?cluster=5931711459859272834&hl=en&as_sdt=0,5
| 3 | 2,020 |
Self-supervised Co-Training for Video Representation Learning
| 322 |
neurips
| 32 | 4 |
2023-06-16 15:10:26.923000
|
https://github.com/TengdaHan/CoCLR
| 274 |
Self-supervised co-training for video representation learning
|
https://scholar.google.com/scholar?cluster=11310050495628333190&hl=en&as_sdt=0,5
| 13 | 2,020 |
Gradient Estimation with Stochastic Softmax Tricks
| 53 |
neurips
| 5 | 2 |
2023-06-16 15:10:27.115000
|
https://github.com/choidami/sst
| 48 |
Gradient estimation with stochastic softmax tricks
|
https://scholar.google.com/scholar?cluster=3158119995430472666&hl=en&as_sdt=0,38
| 2 | 2,020 |
Meta-Learning Requires Meta-Augmentation
| 65 |
neurips
| 7,320 | 1,025 |
2023-06-16 15:10:27.307000
|
https://github.com/google-research/google-research
| 29,776 |
Meta-learning requires meta-augmentation
|
https://scholar.google.com/scholar?cluster=14551438470205957966&hl=en&as_sdt=0,5
| 727 | 2,020 |
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting
| 20 |
neurips
| 5 | 3 |
2023-06-16 15:10:27.499000
|
https://github.com/Holmeswww/PPOGAN
| 24 |
Improving gan training with probability ratio clipping and sample reweighting
|
https://scholar.google.com/scholar?cluster=1603102881023302087&hl=en&as_sdt=0,5
| 3 | 2,020 |
On Testing of Samplers
| 7 |
neurips
| 1 | 4 |
2023-06-16 15:10:27.691000
|
https://github.com/meelgroup/barbarik
| 11 |
On testing of samplers
|
https://scholar.google.com/scholar?cluster=5212652190142141590&hl=en&as_sdt=0,31
| 5 | 2,020 |
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
| 4 |
neurips
| 0 | 0 |
2023-06-16 15:10:27.883000
|
https://github.com/ntienvu/tvo_gp_bandit
| 1 |
Gaussian process bandit optimization of the thermodynamic variational objective
|
https://scholar.google.com/scholar?cluster=4199760950647121080&hl=en&as_sdt=0,5
| 2 | 2,020 |
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
| 503 |
neurips
| 1,867 | 362 |
2023-06-16 15:10:28.076000
|
https://github.com/microsoft/unilm
| 12,770 |
Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers
|
https://scholar.google.com/scholar?cluster=14860866195704248914&hl=en&as_sdt=0,6
| 260 | 2,020 |
Woodbury Transformations for Deep Generative Flows
| 12 |
neurips
| 0 | 0 |
2023-06-16 15:10:28.271000
|
https://github.com/yolu1055/WoodburyTransformations
| 2 |
Woodbury transformations for deep generative flows
|
https://scholar.google.com/scholar?cluster=5314675607084921976&hl=en&as_sdt=0,5
| 4 | 2,020 |
Graph Contrastive Learning with Augmentations
| 864 |
neurips
| 90 | 27 |
2023-06-16 15:10:28.466000
|
https://github.com/Shen-Lab/GraphCL
| 434 |
Graph contrastive learning with augmentations
|
https://scholar.google.com/scholar?cluster=9963871328827947371&hl=en&as_sdt=0,33
| 9 | 2,020 |
Gradient Surgery for Multi-Task Learning
| 483 |
neurips
| 37 | 12 |
2023-06-16 15:10:28.658000
|
https://github.com/tianheyu927/PCGrad
| 255 |
Gradient surgery for multi-task learning
|
https://scholar.google.com/scholar?cluster=15639381935804051305&hl=en&as_sdt=0,5
| 18 | 2,020 |
Bayesian Probabilistic Numerical Integration with Tree-Based Models
| 2 |
neurips
| 1 | 0 |
2023-06-16 15:10:28.850000
|
https://github.com/ImperialCollegeLondon/BART-Int
| 7 |
Bayesian probabilistic numerical integration with tree-based models
|
https://scholar.google.com/scholar?cluster=17070012166494581814&hl=en&as_sdt=0,5
| 3 | 2,020 |
Graph Meta Learning via Local Subgraphs
| 99 |
neurips
| 28 | 3 |
2023-06-16 15:10:29.042000
|
https://github.com/mims-harvard/g-meta
| 105 |
Graph meta learning via local subgraphs
|
https://scholar.google.com/scholar?cluster=12205589678815319348&hl=en&as_sdt=0,33
| 6 | 2,020 |
Stochastic Deep Gaussian Processes over Graphs
| 12 |
neurips
| 4 | 2 |
2023-06-16 15:10:29.239000
|
https://github.com/naiqili/DGPG
| 21 |
Stochastic deep gaussian processes over graphs
|
https://scholar.google.com/scholar?cluster=12355545301307730680&hl=en&as_sdt=0,5
| 1 | 2,020 |
Evaluating Attribution for Graph Neural Networks
| 73 |
neurips
| 16 | 2 |
2023-06-16 15:10:29.431000
|
https://github.com/google-research/graph-attribution
| 65 |
Evaluating attribution for graph neural networks
|
https://scholar.google.com/scholar?cluster=8947730950192198028&hl=en&as_sdt=0,5
| 7 | 2,020 |
Neuron Shapley: Discovering the Responsible Neurons
| 73 |
neurips
| 4 | 1 |
2023-06-16 15:10:29.623000
|
https://github.com/amiratag/neuronshapley
| 21 |
Neuron shapley: Discovering the responsible neurons
|
https://scholar.google.com/scholar?cluster=17071194082042236550&hl=en&as_sdt=0,5
| 3 | 2,020 |
Stochastic Normalizing Flows
| 93 |
neurips
| 9 | 1 |
2023-06-16 15:10:29.816000
|
https://github.com/noegroup/stochastic_normalizing_flows
| 56 |
Stochastic normalizing flows
|
https://scholar.google.com/scholar?cluster=16849056708118710462&hl=en&as_sdt=0,22
| 5 | 2,020 |
Revisiting Parameter Sharing for Automatic Neural Channel Number Search
| 26 |
neurips
| 7 | 0 |
2023-06-16 15:10:30.008000
|
https://github.com/haolibai/APS-channel-search
| 20 |
Revisiting parameter sharing for automatic neural channel number search
|
https://scholar.google.com/scholar?cluster=13186156999876305193&hl=en&as_sdt=0,1
| 3 | 2,020 |
Differentially-Private Federated Linear Bandits
| 78 |
neurips
| 3 | 1 |
2023-06-16 15:10:30.201000
|
https://github.com/abhimanyudubey/private_federated_linear_bandits
| 3 |
Differentially-private federated linear bandits
|
https://scholar.google.com/scholar?cluster=10188063075897991616&hl=en&as_sdt=0,5
| 1 | 2,020 |
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking
| 31 |
neurips
| 10 | 18 |
2023-06-16 15:10:30.394000
|
https://github.com/paninski-lab/deepgraphpose
| 29 |
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking
|
https://scholar.google.com/scholar?cluster=3453822722256675361&hl=en&as_sdt=0,44
| 7 | 2,020 |
Sparse Symplectically Integrated Neural Networks
| 20 |
neurips
| 1 | 0 |
2023-06-16 15:10:30.586000
|
https://github.com/dandip/ssinn
| 8 |
Sparse symplectically integrated neural networks
|
https://scholar.google.com/scholar?cluster=14798517979957496479&hl=en&as_sdt=0,15
| 2 | 2,020 |
Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision
| 16 |
neurips
| 2 | 0 |
2023-06-16 15:10:30.778000
|
https://github.com/nicolaihaeni/corn
| 14 |
Continuous object representation networks: novel view synthesis without target view supervision
|
https://scholar.google.com/scholar?cluster=765897047698290451&hl=en&as_sdt=0,5
| 2 | 2,020 |
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
| 41 |
neurips
| 4 | 2 |
2023-06-16 15:10:30.970000
|
https://github.com/thomassutter/mmjsd
| 13 |
Multimodal generative learning utilizing jensen-shannon-divergence
|
https://scholar.google.com/scholar?cluster=17836611088871038657&hl=en&as_sdt=0,5
| 1 | 2,020 |
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers
| 134 |
neurips
| 23 | 0 |
2023-06-16 15:10:31.162000
|
https://github.com/tum-pbs/Solver-in-the-Loop
| 122 |
Solver-in-the-loop: Learning from differentiable physics to interact with iterative pde-solvers
|
https://scholar.google.com/scholar?cluster=2286766760551989039&hl=en&as_sdt=0,32
| 6 | 2,020 |
Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield
| 4 |
neurips
| 1 | 0 |
2023-06-16 15:10:31.356000
|
https://github.com/pesaranlab/neuro_cbs
| 6 |
Optimal adaptive electrode selection to maximize simultaneously recorded neuron yield
|
https://scholar.google.com/scholar?cluster=3860529084779056549&hl=en&as_sdt=0,10
| 3 | 2,020 |
Neurosymbolic Reinforcement Learning with Formally Verified Exploration
| 50 |
neurips
| 2 | 5 |
2023-06-16 15:10:31.548000
|
https://github.com/gavlegoat/safe-learning
| 13 |
Neurosymbolic reinforcement learning with formally verified exploration
|
https://scholar.google.com/scholar?cluster=16428305531344128935&hl=en&as_sdt=0,5
| 2 | 2,020 |
On 1/n neural representation and robustness
| 19 |
neurips
| 1 | 0 |
2023-06-16 15:10:31.742000
|
https://github.com/josuenassar/power_law
| 7 |
On 1/n neural representation and robustness
|
https://scholar.google.com/scholar?cluster=14612770369819484609&hl=en&as_sdt=0,14
| 3 | 2,020 |
Boundary thickness and robustness in learning models
| 27 |
neurips
| 1 | 0 |
2023-06-16 15:10:31.934000
|
https://github.com/nsfzyzz/boundary_thickness
| 17 |
Boundary thickness and robustness in learning models
|
https://scholar.google.com/scholar?cluster=7743383416741324781&hl=en&as_sdt=0,14
| 2 | 2,020 |
Demixed shared component analysis of neural population data from multiple brain areas
| 0 |
neurips
| 1 | 1 |
2023-06-16 15:10:32.126000
|
https://github.com/yu-takagi/dSCA
| 10 |
Demixed shared component analysis of neural population data from multiple brain areas
|
https://scholar.google.com/scholar?cluster=14678847289626964830&hl=en&as_sdt=0,34
| 3 | 2,020 |
Learning Kernel Tests Without Data Splitting
| 14 |
neurips
| 2 | 0 |
2023-06-16 15:10:32.320000
|
https://github.com/MPI-IS/tests-wo-splitting
| 5 |
Learning kernel tests without data splitting
|
https://scholar.google.com/scholar?cluster=12039043020526096218&hl=en&as_sdt=0,14
| 3 | 2,020 |
Unsupervised Data Augmentation for Consistency Training
| 1,590 |
neurips
| 313 | 71 |
2023-06-16 15:10:32.512000
|
https://github.com/google-research/uda
| 2,122 |
Unsupervised data augmentation for consistency training
|
https://scholar.google.com/scholar?cluster=12880251999793471515&hl=en&as_sdt=0,22
| 44 | 2,020 |
Pruning neural networks without any data by iteratively conserving synaptic flow
| 337 |
neurips
| 42 | 4 |
2023-06-16 15:10:32.704000
|
https://github.com/ganguli-lab/Synaptic-Flow
| 190 |
Pruning neural networks without any data by iteratively conserving synaptic flow
|
https://scholar.google.com/scholar?cluster=1210718401723821316&hl=en&as_sdt=0,39
| 27 | 2,020 |
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