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Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models
| 8 |
neurips
| 0 | 1 |
2023-06-16 16:07:53.483000
|
https://github.com/echoyi/rps_lje
| 2 |
Representer point selection via local jacobian expansion for post-hoc classifier explanation of deep neural networks and ensemble models
|
https://scholar.google.com/scholar?cluster=10184783151152200562&hl=en&as_sdt=0,5
| 3 | 2,021 |
Editing a classifier by rewriting its prediction rules
| 33 |
neurips
| 7 | 0 |
2023-06-16 16:07:53.684000
|
https://github.com/madrylab/editingclassifiers
| 88 |
Editing a classifier by rewriting its prediction rules
|
https://scholar.google.com/scholar?cluster=10393645433715100130&hl=en&as_sdt=0,5
| 6 | 2,021 |
How Modular should Neural Module Networks Be for Systematic Generalization?
| 5 |
neurips
| 0 | 0 |
2023-06-16 16:07:53.885000
|
https://github.com/vanessadamario/understanding_reasoning
| 6 |
How Modular Should Neural Module Networks Be for Systematic Generalization?
|
https://scholar.google.com/scholar?cluster=1661765216246697940&hl=en&as_sdt=0,5
| 2 | 2,021 |
The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization
| 4 |
neurips
| 1 | 0 |
2023-06-16 16:07:54.096000
|
https://github.com/dlej/adaptive-dropout
| 0 |
The flip side of the reweighted coin: duality of adaptive dropout and regularization
|
https://scholar.google.com/scholar?cluster=7949218782652631707&hl=en&as_sdt=0,5
| 1 | 2,021 |
Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs
| 21 |
neurips
| 3 | 3 |
2023-06-16 16:07:54.300000
|
https://github.com/akirato/perm-gaussiankg
| 9 |
Probabilistic entity representation model for reasoning over knowledge graphs
|
https://scholar.google.com/scholar?cluster=3279393825125769301&hl=en&as_sdt=0,5
| 1 | 2,021 |
Black Box Probabilistic Numerics
| 2 |
neurips
| 0 | 0 |
2023-06-16 16:07:54.502000
|
https://github.com/oteym/bbpn
| 0 |
Black box probabilistic numerics
|
https://scholar.google.com/scholar?cluster=11244542960585978883&hl=en&as_sdt=0,5
| 1 | 2,021 |
Interpolation can hurt robust generalization even when there is no noise
| 8 |
neurips
| 0 | 0 |
2023-06-16 16:07:54.706000
|
https://github.com/michaelaerni/interpolation_robustness
| 1 |
Interpolation can hurt robust generalization even when there is no noise
|
https://scholar.google.com/scholar?cluster=15775630453700777923&hl=en&as_sdt=0,5
| 2 | 2,021 |
On the Equivalence between Neural Network and Support Vector Machine
| 8 |
neurips
| 3 | 0 |
2023-06-16 16:07:54.910000
|
https://github.com/leslie-ch/equiv-nn-svm
| 8 |
On the equivalence between neural network and support vector machine
|
https://scholar.google.com/scholar?cluster=13784067833914528352&hl=en&as_sdt=0,5
| 2 | 2,021 |
Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training
| 50 |
neurips
| 316 | 30 |
2023-06-16 16:07:55.125000
|
https://github.com/POSTECH-CVLab/PyTorch-StudioGAN
| 3,190 |
Rebooting acgan: Auxiliary classifier gans with stable training
|
https://scholar.google.com/scholar?cluster=15126723779815766107&hl=en&as_sdt=0,10
| 52 | 2,021 |
Robust and Decomposable Average Precision for Image Retrieval
| 13 |
neurips
| 9 | 0 |
2023-06-16 16:07:55.326000
|
https://github.com/elias-ramzi/roadmap
| 70 |
Robust and decomposable average precision for image retrieval
|
https://scholar.google.com/scholar?cluster=16259594709481566013&hl=en&as_sdt=0,5
| 4 | 2,021 |
Spatio-Temporal Variational Gaussian Processes
| 15 |
neurips
| 1 | 1 |
2023-06-16 16:07:55.528000
|
https://github.com/aaltoml/spatio-temporal-gps
| 30 |
Spatio-temporal variational Gaussian processes
|
https://scholar.google.com/scholar?cluster=5327408766327785744&hl=en&as_sdt=0,31
| 2 | 2,021 |
Fast Approximate Dynamic Programming for Infinite-Horizon Markov Decision Processes
| 2 |
neurips
| 1 | 0 |
2023-06-16 16:07:55.728000
|
https://github.com/AminKolarijani/ConjVI
| 0 |
Fast Approximate Dynamic Programming for Infinite-Horizon Markov Decision Processes
|
https://scholar.google.com/scholar?cluster=16725357238288679502&hl=en&as_sdt=0,47
| 1 | 2,021 |
Adaptive Risk Minimization: Learning to Adapt to Domain Shift
| 78 |
neurips
| 24 | 3 |
2023-06-16 16:07:55.959000
|
https://github.com/henrikmarklund/arm
| 78 |
Adaptive risk minimization: Learning to adapt to domain shift
|
https://scholar.google.com/scholar?cluster=6509702681777063562&hl=en&as_sdt=0,5
| 7 | 2,021 |
Learning State Representations from Random Deep Action-conditional Predictions
| 3 |
neurips
| 0 | 0 |
2023-06-16 16:07:56.163000
|
https://github.com/Hwhitetooth/random_gvfs
| 3 |
Learning state representations from random deep action-conditional predictions
|
https://scholar.google.com/scholar?cluster=15623109071018458033&hl=en&as_sdt=0,5
| 3 | 2,021 |
Tracking People with 3D Representations
| 19 |
neurips
| 6 | 6 |
2023-06-16 16:07:56.363000
|
https://github.com/brjathu/T3DP
| 83 |
Tracking people with 3D representations
|
https://scholar.google.com/scholar?cluster=18142751187854037322&hl=en&as_sdt=0,36
| 4 | 2,021 |
Optimal Sketching for Trace Estimation
| 8 |
neurips
| 0 | 0 |
2023-06-16 16:07:56.564000
|
https://github.com/11hifish/OptSketchTraceEst
| 1 |
Optimal sketching for trace estimation
|
https://scholar.google.com/scholar?cluster=1153169636268932836&hl=en&as_sdt=0,5
| 2 | 2,021 |
Estimating Multi-cause Treatment Effects via Single-cause Perturbation
| 8 |
neurips
| 1 | 0 |
2023-06-16 16:07:56.764000
|
https://github.com/zhaozhiqian/single-cause-perturbation-neurips-2021
| 9 |
Estimating multi-cause treatment effects via single-cause perturbation
|
https://scholar.google.com/scholar?cluster=15417661006229778320&hl=en&as_sdt=0,5
| 2 | 2,021 |
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
| 20 |
neurips
| 1 | 0 |
2023-06-16 16:07:56.964000
|
https://github.com/vanderschaarlab/miracle
| 16 |
Miracle: Causally-aware imputation via learning missing data mechanisms
|
https://scholar.google.com/scholar?cluster=637656559224861079&hl=en&as_sdt=0,5
| 1 | 2,021 |
Efficient Training of Visual Transformers with Small Datasets
| 86 |
neurips
| 11 | 1 |
2023-06-16 16:07:57.164000
|
https://github.com/yhlleo/VTs-Drloc
| 124 |
Efficient training of visual transformers with small datasets
|
https://scholar.google.com/scholar?cluster=17891879498080154736&hl=en&as_sdt=0,5
| 3 | 2,021 |
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud Registration
| 54 |
neurips
| 8 | 1 |
2023-06-16 16:07:57.365000
|
https://github.com/haoyu94/coarse-to-fine-correspondences
| 79 |
Cofinet: Reliable coarse-to-fine correspondences for robust pointcloud registration
|
https://scholar.google.com/scholar?cluster=11101496447247741194&hl=en&as_sdt=0,5
| 7 | 2,021 |
Partial success in closing the gap between human and machine vision
| 87 |
neurips
| 31 | 3 |
2023-06-16 16:07:57.565000
|
https://github.com/bethgelab/model-vs-human
| 286 |
Partial success in closing the gap between human and machine vision
|
https://scholar.google.com/scholar?cluster=875131557547078483&hl=en&as_sdt=0,44
| 14 | 2,021 |
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
| 7 |
neurips
| 2 | 0 |
2023-06-16 16:07:57.765000
|
https://github.com/RAIVNLab/LLC
| 10 |
Llc: Accurate, multi-purpose learnt low-dimensional binary codes
|
https://scholar.google.com/scholar?cluster=13039200529155817900&hl=en&as_sdt=0,26
| 7 | 2,021 |
Well-tuned Simple Nets Excel on Tabular Datasets
| 65 |
neurips
| 12 | 2 |
2023-06-16 16:07:57.966000
|
https://github.com/releaunifreiburg/WellTunedSimpleNets
| 61 |
Well-tuned simple nets excel on tabular datasets
|
https://scholar.google.com/scholar?cluster=3278110535551285021&hl=en&as_sdt=0,5
| 0 | 2,021 |
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples
| 17 |
neurips
| 0 | 0 |
2023-06-16 16:07:58.166000
|
https://github.com/lehduong/poodle
| 15 |
Poodle: Improving few-shot learning via penalizing out-of-distribution samples
|
https://scholar.google.com/scholar?cluster=3110608132459166392&hl=en&as_sdt=0,5
| 1 | 2,021 |
Densely connected normalizing flows
| 27 |
neurips
| 9 | 0 |
2023-06-16 16:07:58.369000
|
https://github.com/matejgrcic/DenseFlow
| 32 |
Densely connected normalizing flows
|
https://scholar.google.com/scholar?cluster=12123857522303227293&hl=en&as_sdt=0,32
| 4 | 2,021 |
Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing
| 8 |
neurips
| 0 | 0 |
2023-06-16 16:07:58.569000
|
https://github.com/thecharlieblake/snowflake
| 4 |
Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing
|
https://scholar.google.com/scholar?cluster=12712996642787863150&hl=en&as_sdt=0,5
| 1 | 2,021 |
VAST: Value Function Factorization with Variable Agent Sub-Teams
| 5 |
neurips
| 1 | 0 |
2023-06-16 16:07:58.769000
|
https://github.com/thomyphan/scalable-marl
| 4 |
Vast: Value function factorization with variable agent sub-teams
|
https://scholar.google.com/scholar?cluster=15101436546519629155&hl=en&as_sdt=0,3
| 1 | 2,021 |
Multiwavelet-based Operator Learning for Differential Equations
| 58 |
neurips
| 6 | 1 |
2023-06-16 16:07:58.969000
|
https://github.com/gaurav71531/mwt-operator
| 40 |
Multiwavelet-based operator learning for differential equations
|
https://scholar.google.com/scholar?cluster=15278573285274207764&hl=en&as_sdt=0,5
| 1 | 2,021 |
Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning
| 13 |
neurips
| 6 | 1 |
2023-06-16 16:07:59.170000
|
https://github.com/aakashrkaku/intermdiate_layer_matter_ssl
| 39 |
Intermediate layers matter in momentum contrastive self supervised learning
|
https://scholar.google.com/scholar?cluster=17990388829355645344&hl=en&as_sdt=0,36
| 2 | 2,021 |
Learning Nonparametric Volterra Kernels with Gaussian Processes
| 2 |
neurips
| 2 | 0 |
2023-06-16 16:07:59.371000
|
https://github.com/magnusross/nvkm
| 1 |
Learning nonparametric Volterra kernels with Gaussian processes
|
https://scholar.google.com/scholar?cluster=10898264461292575760&hl=en&as_sdt=0,33
| 3 | 2,021 |
DiBS: Differentiable Bayesian Structure Learning
| 28 |
neurips
| 8 | 1 |
2023-06-16 16:07:59.571000
|
https://github.com/larslorch/dibs
| 35 |
Dibs: Differentiable bayesian structure learning
|
https://scholar.google.com/scholar?cluster=4035014769080983661&hl=en&as_sdt=0,5
| 3 | 2,021 |
Nonparametric estimation of continuous DPPs with kernel methods
| 2 |
neurips
| 0 | 0 |
2023-06-16 16:07:59.771000
|
https://github.com/mrfanuel/learningcontinuousdpps.jl
| 0 |
Nonparametric estimation of continuous DPPs with kernel methods
|
https://scholar.google.com/scholar?cluster=4870049229735004027&hl=en&as_sdt=0,5
| 2 | 2,021 |
FINE Samples for Learning with Noisy Labels
| 37 |
neurips
| 11 | 1 |
2023-06-16 16:07:59.972000
|
https://github.com/Kthyeon/FINE_official
| 28 |
Fine samples for learning with noisy labels
|
https://scholar.google.com/scholar?cluster=5795819026441834181&hl=en&as_sdt=0,1
| 3 | 2,021 |
Residual2Vec: Debiasing graph embedding with random graphs
| 8 |
neurips
| 1 | 1 |
2023-06-16 16:08:00.173000
|
https://github.com/skojaku/residual2vec
| 5 |
Residual2Vec: Debiasing graph embedding with random graphs
|
https://scholar.google.com/scholar?cluster=741770936150407440&hl=en&as_sdt=0,48
| 3 | 2,021 |
Training Neural Networks with Fixed Sparse Masks
| 47 |
neurips
| 1 | 1 |
2023-06-16 16:08:00.372000
|
https://github.com/varunnair18/fish
| 44 |
Training neural networks with fixed sparse masks
|
https://scholar.google.com/scholar?cluster=16194905137327399007&hl=en&as_sdt=0,3
| 5 | 2,021 |
Learning to Schedule Heuristics in Branch and Bound
| 27 |
neurips
| 0 | 0 |
2023-06-16 16:08:00.573000
|
https://github.com/antoniach/heuristic-scheduling
| 2 |
Learning to schedule heuristics in branch and bound
|
https://scholar.google.com/scholar?cluster=5910831186806034579&hl=en&as_sdt=0,5
| 1 | 2,021 |
On Training Implicit Models
| 31 |
neurips
| 0 | 0 |
2023-06-16 16:08:00.773000
|
https://github.com/gsunshine/phantom_grad
| 3 |
On training implicit models
|
https://scholar.google.com/scholar?cluster=15707261069141178694&hl=en&as_sdt=0,33
| 1 | 2,021 |
MLP-Mixer: An all-MLP Architecture for Vision
| 1,181 |
neurips
| 976 | 108 |
2023-06-16 16:08:00.973000
|
https://github.com/google-research/vision_transformer
| 7,383 |
Mlp-mixer: An all-mlp architecture for vision
|
https://scholar.google.com/scholar?cluster=10553738615668616847&hl=en&as_sdt=0,10
| 83 | 2,021 |
A Framework to Learn with Interpretation
| 19 |
neurips
| 0 | 0 |
2023-06-16 16:08:01.173000
|
https://github.com/jayneelparekh/flint
| 4 |
A framework to learn with interpretation
|
https://scholar.google.com/scholar?cluster=4070242673228533811&hl=en&as_sdt=0,44
| 2 | 2,021 |
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective
| 34 |
neurips
| 10 | 2 |
2023-06-16 16:08:01.376000
|
https://github.com/kamwoh/orthohash
| 86 |
One loss for all: Deep hashing with a single cosine similarity based learning objective
|
https://scholar.google.com/scholar?cluster=2583147407697394986&hl=en&as_sdt=0,47
| 6 | 2,021 |
Discovering and Achieving Goals via World Models
| 45 |
neurips
| 16 | 0 |
2023-06-16 16:08:01.577000
|
https://github.com/orybkin/lexa
| 76 |
Discovering and achieving goals via world models
|
https://scholar.google.com/scholar?cluster=5829288564563555127&hl=en&as_sdt=0,33
| 5 | 2,021 |
Understanding and Improving Early Stopping for Learning with Noisy Labels
| 68 |
neurips
| 4 | 0 |
2023-06-16 16:08:01.779000
|
https://github.com/tmllab/PES
| 21 |
Understanding and improving early stopping for learning with noisy labels
|
https://scholar.google.com/scholar?cluster=15957250689455234622&hl=en&as_sdt=0,5
| 1 | 2,021 |
On the Power of Edge Independent Graph Models
| 4 |
neurips
| 1 | 0 |
2023-06-16 16:08:01.985000
|
https://github.com/konsotirop/edge_independent_models
| 0 |
On the power of edge independent graph models
|
https://scholar.google.com/scholar?cluster=18323628081237600189&hl=en&as_sdt=0,43
| 1 | 2,021 |
Understanding Adaptive, Multiscale Temporal Integration In Deep Speech Recognition Systems
| 2 |
neurips
| 2 | 0 |
2023-06-16 16:08:02.186000
|
https://github.com/naplab/pytci
| 5 |
Understanding adaptive, multiscale temporal integration in deep speech recognition systems
|
https://scholar.google.com/scholar?cluster=12420066153878945080&hl=en&as_sdt=0,5
| 3 | 2,021 |
VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer
| 16 |
neurips
| 8 | 1 |
2023-06-16 16:08:02.395000
|
https://github.com/zinengtang/VidLanKD
| 56 |
Vidlankd: Improving language understanding via video-distilled knowledge transfer
|
https://scholar.google.com/scholar?cluster=7463854148128804617&hl=en&as_sdt=0,5
| 4 | 2,021 |
Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess
| 14 |
neurips
| 0 | 0 |
2023-06-16 16:08:02.607000
|
https://github.com/csslab/behavioral-stylometry
| 13 |
Detecting individual decision-making style: Exploring behavioral stylometry in chess
|
https://scholar.google.com/scholar?cluster=5114217380206270337&hl=en&as_sdt=0,10
| 4 | 2,021 |
AutoGEL: An Automated Graph Neural Network with Explicit Link Information
| 19 |
neurips
| 1 | 0 |
2023-06-16 16:08:02.809000
|
https://github.com/zwangeo/autogel
| 8 |
Autogel: An automated graph neural network with explicit link information
|
https://scholar.google.com/scholar?cluster=17230311752348468985&hl=en&as_sdt=0,5
| 2 | 2,021 |
Recognizing Vector Graphics without Rasterization
| 6 |
neurips
| 12 | 2 |
2023-06-16 16:08:03.009000
|
https://github.com/microsoft/YOLaT-VectorGraphicsRecognition
| 59 |
Recognizing vector graphics without rasterization
|
https://scholar.google.com/scholar?cluster=15241098815827282500&hl=en&as_sdt=0,5
| 7 | 2,021 |
On Episodes, Prototypical Networks, and Few-Shot Learning
| 45 |
neurips
| 4 | 1 |
2023-06-16 16:08:03.210000
|
https://github.com/fiveai/on-episodes-fsl
| 26 |
On episodes, prototypical networks, and few-shot learning
|
https://scholar.google.com/scholar?cluster=7793453768259983774&hl=en&as_sdt=0,5
| 7 | 2,021 |
CHIP: CHannel Independence-based Pruning for Compact Neural Networks
| 51 |
neurips
| 5 | 3 |
2023-06-16 16:08:03.411000
|
https://github.com/eclipsess/chip_neurips2021
| 18 |
Chip: Channel independence-based pruning for compact neural networks
|
https://scholar.google.com/scholar?cluster=8136547128458704716&hl=en&as_sdt=0,33
| 3 | 2,021 |
Active Offline Policy Selection
| 11 |
neurips
| 2 | 0 |
2023-06-16 16:08:03.611000
|
https://github.com/deepmind/active_ops
| 29 |
Active offline policy selection
|
https://scholar.google.com/scholar?cluster=11479789843875532495&hl=en&as_sdt=0,5
| 5 | 2,021 |
Information-theoretic generalization bounds for black-box learning algorithms
| 19 |
neurips
| 1 | 0 |
2023-06-16 16:08:03.812000
|
https://github.com/hrayrhar/f-cmi
| 3 |
Information-theoretic generalization bounds for black-box learning algorithms
|
https://scholar.google.com/scholar?cluster=17028084888610967844&hl=en&as_sdt=0,5
| 5 | 2,021 |
Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation
| 6 |
neurips
| 4 | 0 |
2023-06-16 16:08:04.038000
|
https://github.com/mingcv/ytmt-strategy
| 39 |
Trash or treasure? an interactive dual-stream strategy for single image reflection separation
|
https://scholar.google.com/scholar?cluster=18395690050017455415&hl=en&as_sdt=0,5
| 2 | 2,021 |
Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding
| 22 |
neurips
| 1 | 0 |
2023-06-16 16:08:04.239000
|
https://github.com/tewiSong/Rot-Pro
| 10 |
Rot-pro: Modeling transitivity by projection in knowledge graph embedding
|
https://scholar.google.com/scholar?cluster=11215289012161533976&hl=en&as_sdt=0,33
| 1 | 2,021 |
Modular Gaussian Processes for Transfer Learning
| 4 |
neurips
| 1 | 0 |
2023-06-16 16:08:04.439000
|
https://github.com/pmorenoz/modulargp
| 13 |
Modular Gaussian processes for transfer learning
|
https://scholar.google.com/scholar?cluster=2796305591602379959&hl=en&as_sdt=0,33
| 2 | 2,021 |
Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering
| 68 |
neurips
| 11 | 8 |
2023-06-16 16:08:04.639000
|
https://github.com/YoungJoongUNC/Neural_Human_Performer
| 112 |
Neural human performer: Learning generalizable radiance fields for human performance rendering
|
https://scholar.google.com/scholar?cluster=7942977182226378581&hl=en&as_sdt=0,5
| 10 | 2,021 |
Asymptotics of representation learning in finite Bayesian neural networks
| 21 |
neurips
| 1 | 0 |
2023-06-16 16:08:04.839000
|
https://github.com/pehlevan-group/finite-width-bayesian
| 2 |
Asymptotics of representation learning in finite Bayesian neural networks
|
https://scholar.google.com/scholar?cluster=3625210166021367573&hl=en&as_sdt=0,33
| 2 | 2,021 |
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?
| 20 |
neurips
| 2 | 1 |
2023-06-16 16:08:05.050000
|
https://github.com/dmirlab-group/dsan
| 19 |
Domain adaptation with invariant representation learning: What transformations to learn?
|
https://scholar.google.com/scholar?cluster=10398669082966342781&hl=en&as_sdt=0,33
| 3 | 2,021 |
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
| 91 |
neurips
| 40 | 3 |
2023-06-16 16:08:05.252000
|
https://github.com/ermongroup/csdi
| 148 |
CSDI: Conditional score-based diffusion models for probabilistic time series imputation
|
https://scholar.google.com/scholar?cluster=3890787205229522603&hl=en&as_sdt=0,5
| 8 | 2,021 |
Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging
| 7 |
neurips
| 2 | 0 |
2023-06-16 16:08:05.452000
|
https://github.com/alihashemi-ai/dugh-neurips-2021
| 4 |
Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging
|
https://scholar.google.com/scholar?cluster=6986870933699161127&hl=en&as_sdt=0,11
| 1 | 2,021 |
Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond Kernels
| 13 |
neurips
| 0 | 0 |
2023-06-16 16:08:05.653000
|
https://github.com/skarp/local-signal-adaptivity
| 1 |
Local signal adaptivity: Provable feature learning in neural networks beyond kernels
|
https://scholar.google.com/scholar?cluster=5974588458999600841&hl=en&as_sdt=0,39
| 1 | 2,021 |
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding
| 12 |
neurips
| 87 | 8 |
2023-06-16 16:08:05.853000
|
https://github.com/brendenpetersen/deep-symbolic-optimization
| 374 |
Symbolic regression via deep reinforcement learning enhanced genetic programming seeding
|
https://scholar.google.com/scholar?cluster=17727261586296192952&hl=en&as_sdt=0,5
| 12 | 2,021 |
Choose a Transformer: Fourier or Galerkin
| 49 |
neurips
| 23 | 1 |
2023-06-16 16:08:06.054000
|
https://github.com/scaomath/galerkin-transformer
| 172 |
Choose a transformer: Fourier or galerkin
|
https://scholar.google.com/scholar?cluster=8571374970772054230&hl=en&as_sdt=0,43
| 6 | 2,021 |
Canonical Capsules: Self-Supervised Capsules in Canonical Pose
| 33 |
neurips
| 21 | 1 |
2023-06-16 16:08:06.254000
|
https://github.com/canonical-capsules/canonical-capsules
| 168 |
Canonical capsules: Self-supervised capsules in canonical pose
|
https://scholar.google.com/scholar?cluster=8210427563278866334&hl=en&as_sdt=0,5
| 15 | 2,021 |
Dynamics-regulated kinematic policy for egocentric pose estimation
| 27 |
neurips
| 5 | 0 |
2023-06-16 16:08:06.455000
|
https://github.com/KlabCMU/kin-poly
| 64 |
Dynamics-regulated kinematic policy for egocentric pose estimation
|
https://scholar.google.com/scholar?cluster=3653129200622032279&hl=en&as_sdt=0,33
| 8 | 2,021 |
Not All Low-Pass Filters are Robust in Graph Convolutional Networks
| 21 |
neurips
| 1 | 1 |
2023-06-16 16:08:06.655000
|
https://github.com/swiftieh/lfr
| 8 |
Not all low-pass filters are robust in graph convolutional networks
|
https://scholar.google.com/scholar?cluster=931846674338665597&hl=en&as_sdt=0,47
| 2 | 2,021 |
Counterfactual Maximum Likelihood Estimation for Training Deep Networks
| 3 |
neurips
| 1 | 1 |
2023-06-16 16:08:06.856000
|
https://github.com/WANGXinyiLinda/CMLE
| 9 |
Counterfactual Maximum Likelihood Estimation for Training Deep Networks
|
https://scholar.google.com/scholar?cluster=12195718352241039351&hl=en&as_sdt=0,34
| 1 | 2,021 |
Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks
| 7 |
neurips
| 3 | 1 |
2023-06-16 16:08:07.056000
|
https://github.com/gsimchoni/lmmnn
| 15 |
Using random effects to account for high-cardinality categorical features and repeated measures in deep neural networks
|
https://scholar.google.com/scholar?cluster=6823564351084758904&hl=en&as_sdt=0,3
| 2 | 2,021 |
Learning the optimal Tikhonov regularizer for inverse problems
| 10 |
neurips
| 1 | 0 |
2023-06-16 16:08:07.256000
|
https://github.com/LearnTikhonov/Code
| 2 |
Learning the optimal Tikhonov regularizer for inverse problems
|
https://scholar.google.com/scholar?cluster=4351597932105828079&hl=en&as_sdt=0,3
| 1 | 2,021 |
NovelD: A Simple yet Effective Exploration Criterion
| 27 |
neurips
| 4 | 1 |
2023-06-16 16:08:07.456000
|
https://github.com/tianjunz/NovelD
| 32 |
Noveld: A simple yet effective exploration criterion
|
https://scholar.google.com/scholar?cluster=5494596245419796169&hl=en&as_sdt=0,5
| 3 | 2,021 |
Second-Order Neural ODE Optimizer
| 8 |
neurips
| 7 | 0 |
2023-06-16 16:08:07.657000
|
https://github.com/ghliu/snopt
| 40 |
Second-order neural ode optimizer
|
https://scholar.google.com/scholar?cluster=440731558768338090&hl=en&as_sdt=0,26
| 2 | 2,021 |
Dense Unsupervised Learning for Video Segmentation
| 15 |
neurips
| 21 | 2 |
2023-06-16 16:08:07.858000
|
https://github.com/visinf/dense-ulearn-vos
| 178 |
Dense unsupervised learning for video segmentation
|
https://scholar.google.com/scholar?cluster=4698820805615701905&hl=en&as_sdt=0,5
| 8 | 2,021 |
Charting and Navigating the Space of Solutions for Recurrent Neural Networks
| 8 |
neurips
| 1 | 0 |
2023-06-16 16:08:08.059000
|
https://github.com/eliaturner/space-of-solutions-rnn
| 0 |
Charting and navigating the space of solutions for recurrent neural networks
|
https://scholar.google.com/scholar?cluster=1383134726251772649&hl=en&as_sdt=0,5
| 2 | 2,021 |
Reusing Combinatorial Structure: Faster Iterative Projections over Submodular Base Polytopes
| 2 |
neurips
| 0 | 0 |
2023-06-16 16:08:08.260000
|
https://github.com/jaimoondra/submodular-polytope-projections
| 0 |
Reusing combinatorial structure: Faster iterative projections over submodular base polytopes
|
https://scholar.google.com/scholar?cluster=4313712568936757155&hl=en&as_sdt=0,39
| 2 | 2,021 |
Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes
| 8 |
neurips
| 0 | 0 |
2023-06-16 16:08:08.461000
|
https://github.com/salusanga/alm-dnn
| 12 |
Constrained optimization to train neural networks on critical and under-represented classes
|
https://scholar.google.com/scholar?cluster=6071197627058372251&hl=en&as_sdt=0,5
| 1 | 2,021 |
Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification
| 6 |
neurips
| 0 | 0 |
2023-06-16 16:08:08.661000
|
https://github.com/clreda/misspecified-top-m
| 0 |
Dealing with misspecification in fixed-confidence linear top-m identification
|
https://scholar.google.com/scholar?cluster=17658923978445131586&hl=en&as_sdt=0,5
| 1 | 2,021 |
Set Prediction in the Latent Space
| 2 |
neurips
| 0 | 0 |
2023-06-16 16:08:08.862000
|
https://github.com/phizaz/latent-set-prediction
| 5 |
Set prediction in the latent space
|
https://scholar.google.com/scholar?cluster=7307560885637402716&hl=en&as_sdt=0,5
| 3 | 2,021 |
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision
| 6 |
neurips
| 6 | 2 |
2023-06-16 16:08:09.062000
|
https://github.com/deepmind/dm_hamiltonian_dynamics_suite
| 28 |
Symetric: measuring the quality of learnt hamiltonian dynamics inferred from vision
|
https://scholar.google.com/scholar?cluster=17033719678461609846&hl=en&as_sdt=0,15
| 5 | 2,021 |
Learning with Holographic Reduced Representations
| 12 |
neurips
| 0 | 2 |
2023-06-16 16:08:09.262000
|
https://github.com/NeuromorphicComputationResearchProgram/Learning-with-Holographic-Reduced-Representations
| 15 |
Learning with holographic reduced representations
|
https://scholar.google.com/scholar?cluster=17605710809418918656&hl=en&as_sdt=0,18
| 2 | 2,021 |
Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations
| 22 |
neurips
| 6 | 1 |
2023-06-16 16:08:09.463000
|
https://github.com/roosephu/crabs
| 12 |
Learning barrier certificates: Towards safe reinforcement learning with zero training-time violations
|
https://scholar.google.com/scholar?cluster=14400533417780078206&hl=en&as_sdt=0,33
| 2 | 2,021 |
On the Second-order Convergence Properties of Random Search Methods
| 3 |
neurips
| 0 | 0 |
2023-06-16 16:08:09.663000
|
https://github.com/adamsolomou/second-order-random-search
| 0 |
On the second-order convergence properties of random search methods
|
https://scholar.google.com/scholar?cluster=13871613628804983300&hl=en&as_sdt=0,33
| 1 | 2,021 |
A Max-Min Entropy Framework for Reinforcement Learning
| 5 |
neurips
| 0 | 0 |
2023-06-16 16:08:09.865000
|
https://github.com/seungyulhan/mme
| 3 |
A max-min entropy framework for reinforcement learning
|
https://scholar.google.com/scholar?cluster=7183103060961218750&hl=en&as_sdt=0,50
| 1 | 2,021 |
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
| 21 |
neurips
| 4 | 0 |
2023-06-16 16:08:10.066000
|
https://github.com/desi-ivanova/idad
| 12 |
Implicit deep adaptive design: policy-based experimental design without likelihoods
|
https://scholar.google.com/scholar?cluster=8438101725055656373&hl=en&as_sdt=0,33
| 1 | 2,021 |
Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis
| 19 |
neurips
| 0 | 0 |
2023-06-16 16:08:10.266000
|
https://github.com/livreq/meta-sgld
| 1 |
Generalization bounds for meta-learning: An information-theoretic analysis
|
https://scholar.google.com/scholar?cluster=15486384152648151886&hl=en&as_sdt=0,5
| 1 | 2,021 |
Identification of the Generalized Condorcet Winner in Multi-dueling Bandits
| 2 |
neurips
| 0 | 0 |
2023-06-16 16:08:10.470000
|
https://github.com/bjoernhad/gcwidentification
| 1 |
Identification of the generalized Condorcet winner in multi-dueling bandits
|
https://scholar.google.com/scholar?cluster=4702390528340000199&hl=en&as_sdt=0,38
| 1 | 2,021 |
Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch
| 12 |
neurips
| 1 | 0 |
2023-06-16 16:08:10.671000
|
https://github.com/lviano/robustmce_irl
| 3 |
Robust inverse reinforcement learning under transition dynamics mismatch
|
https://scholar.google.com/scholar?cluster=6158260538019956069&hl=en&as_sdt=0,5
| 1 | 2,021 |
Post-processing for Individual Fairness
| 32 |
neurips
| 1 | 0 |
2023-06-16 16:08:10.871000
|
https://github.com/felix-petersen/fairness-post-processing
| 5 |
Post-processing for individual fairness
|
https://scholar.google.com/scholar?cluster=4902734240414782212&hl=en&as_sdt=0,33
| 1 | 2,021 |
OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency Regularization
| 26 |
neurips
| 11 | 6 |
2023-06-16 16:08:11.071000
|
https://github.com/VisionLearningGroup/OP_Match
| 45 |
Openmatch: Open-set semi-supervised learning with open-set consistency regularization
|
https://scholar.google.com/scholar?cluster=2362582259050725811&hl=en&as_sdt=0,44
| 2 | 2,021 |
End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
| 63 |
neurips
| 10 | 1 |
2023-06-16 16:08:11.271000
|
https://github.com/DevSinghSachan/emdr2
| 96 |
End-to-end training of multi-document reader and retriever for open-domain question answering
|
https://scholar.google.com/scholar?cluster=6640291202097102131&hl=en&as_sdt=0,33
| 14 | 2,021 |
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
| 36 |
neurips
| 4 | 0 |
2023-06-16 16:08:11.471000
|
https://github.com/fel-thomas/Sobol-Attribution-Method
| 24 |
Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis
|
https://scholar.google.com/scholar?cluster=18305760760422611286&hl=en&as_sdt=0,33
| 2 | 2,021 |
PatchGame: Learning to Signal Mid-level Patches in Referential Games
| 3 |
neurips
| 2 | 0 |
2023-06-16 16:08:11.672000
|
https://github.com/kampta/patchgame
| 22 |
PatchGame: learning to signal mid-level patches in referential games
|
https://scholar.google.com/scholar?cluster=15355548784664334020&hl=en&as_sdt=0,5
| 3 | 2,021 |
Implicit Generative Copulas
| 9 |
neurips
| 1 | 0 |
2023-06-16 16:08:11.873000
|
https://github.com/timcjanke/igc
| 4 |
Implicit generative copulas
|
https://scholar.google.com/scholar?cluster=9521615669512014539&hl=en&as_sdt=0,33
| 1 | 2,021 |
Tensor Normal Training for Deep Learning Models
| 10 |
neurips
| 1 | 0 |
2023-06-16 16:08:12.075000
|
https://github.com/renyiryry/tnt_neurips_2021
| 4 |
Tensor normal training for deep learning models
|
https://scholar.google.com/scholar?cluster=3326882924041786200&hl=en&as_sdt=0,47
| 2 | 2,021 |
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning
| 25 |
neurips
| 1 | 2 |
2023-06-16 16:08:12.279000
|
https://github.com/cuis15/FCFL
| 14 |
Addressing algorithmic disparity and performance inconsistency in federated learning
|
https://scholar.google.com/scholar?cluster=11506353861688134805&hl=en&as_sdt=0,26
| 1 | 2,021 |
Morié Attack (MA): A New Potential Risk of Screen Photos
| 4 |
neurips
| 8 | 3 |
2023-06-16 16:08:12.483000
|
https://github.com/Dantong88/Moire_Attack
| 25 |
Morié attack (ma): A new potential risk of screen photos
|
https://scholar.google.com/scholar?cluster=5204824031822855869&hl=en&as_sdt=0,50
| 1 | 2,021 |
Lattice partition recovery with dyadic CART
| 3 |
neurips
| 0 | 0 |
2023-06-16 16:08:12.684000
|
https://github.com/hernanmp/partition_recovery
| 0 |
Lattice partition recovery with dyadic CART
|
https://scholar.google.com/scholar?cluster=194222908834003497&hl=en&as_sdt=0,33
| 2 | 2,021 |
You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection
| 132 |
neurips
| 101 | 12 |
2023-06-16 16:08:12.886000
|
https://github.com/hustvl/YOLOS
| 716 |
You only look at one sequence: Rethinking transformer in vision through object detection
|
https://scholar.google.com/scholar?cluster=8455459026871994587&hl=en&as_sdt=0,21
| 22 | 2,021 |
Learning to delegate for large-scale vehicle routing
| 33 |
neurips
| 11 | 1 |
2023-06-16 16:08:13.086000
|
https://github.com/mit-wu-lab/learning-to-delegate
| 59 |
Learning to delegate for large-scale vehicle routing
|
https://scholar.google.com/scholar?cluster=3486762460110339204&hl=en&as_sdt=0,33
| 2 | 2,021 |
Towards Context-Agnostic Learning Using Synthetic Data
| 3 |
neurips
| 0 | 0 |
2023-06-16 16:08:13.286000
|
https://github.com/charlesjin/synthetic_data
| 0 |
Towards Context-Agnostic Learning Using Synthetic Data
|
https://scholar.google.com/scholar?cluster=5766633238116465358&hl=en&as_sdt=0,38
| 2 | 2,021 |
Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers
| 3 |
neurips
| 0 | 0 |
2023-06-16 16:08:13.486000
|
https://github.com/blairbilodeau/neurips-2021
| 0 |
Minimax optimal quantile and semi-adversarial regret via root-logarithmic regularizers
|
https://scholar.google.com/scholar?cluster=6590407016231039594&hl=en&as_sdt=0,33
| 1 | 2,021 |
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