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Streaming Radiance Fields for 3D Video Synthesis
| 10 |
neurips
| 5 | 3 |
2023-06-16 22:58:15.484000
|
https://github.com/algohunt/streamrf
| 96 |
Streaming radiance fields for 3d video synthesis
|
https://scholar.google.com/scholar?cluster=1594613451261987052&hl=en&as_sdt=0,14
| 8 | 2,022 |
Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence
| 2 |
neurips
| 1 | 2 |
2023-06-16 22:58:15.696000
|
https://github.com/KU-CVLAB/NeMF
| 70 |
Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence
|
https://scholar.google.com/scholar?cluster=1968290052561441459&hl=en&as_sdt=0,5
| 7 | 2,022 |
Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis
| 3 |
neurips
| 2 | 1 |
2023-06-16 22:58:15.909000
|
https://github.com/mengweiren/longitudinal-representation-learning
| 14 |
Local spatiotemporal representation learning for longitudinally-consistent neuroimage analysis
|
https://scholar.google.com/scholar?cluster=8437472979024832790&hl=en&as_sdt=0,31
| 2 | 2,022 |
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning
| 2 |
neurips
| 2 | 0 |
2023-06-16 22:58:16.122000
|
https://github.com/tliu1997/arnpg-morl
| 5 |
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning
|
https://scholar.google.com/scholar?cluster=15219127852751471694&hl=en&as_sdt=0,23
| 1 | 2,022 |
TAP-Vid: A Benchmark for Tracking Any Point in a Video
| 4 |
neurips
| 19 | 1 |
2023-06-16 22:58:16.336000
|
https://github.com/deepmind/tapnet
| 212 |
TAP-Vid: A Benchmark for Tracking Any Point in a Video
|
https://scholar.google.com/scholar?cluster=17092201381170534981&hl=en&as_sdt=0,33
| 17 | 2,022 |
A Classification of $G$-invariant Shallow Neural Networks
| 5 |
neurips
| 0 | 0 |
2023-06-16 22:58:16.558000
|
https://github.com/dagrawa2/gsnn_classification_code
| 0 |
A Classification of -invariant Shallow Neural Networks
|
https://scholar.google.com/scholar?cluster=11077075131989361762&hl=en&as_sdt=0,33
| 1 | 2,022 |
Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources
| 1 |
neurips
| 0 | 1 |
2023-06-16 22:58:16.810000
|
https://github.com/bariscanbozkurt/biologically-plausible-detmaxnns-for-blind-source-separation
| 0 |
Biologically-plausible determinant maximization neural networks for blind separation of correlated sources
|
https://scholar.google.com/scholar?cluster=1796611740779169279&hl=en&as_sdt=0,5
| 1 | 2,022 |
What Makes Graph Neural Networks Miscalibrated?
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:58:17.023000
|
https://github.com/hans66hsu/gats
| 12 |
What Makes Graph Neural Networks Miscalibrated?
|
https://scholar.google.com/scholar?cluster=18376762019790948001&hl=en&as_sdt=0,5
| 2 | 2,022 |
Stochastic Adaptive Activation Function
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:58:17.241000
|
https://github.com/kyungsu-lee-ksl/ash
| 4 |
Stochastic Adaptive Activation Function
|
https://scholar.google.com/scholar?cluster=12146555690401173811&hl=en&as_sdt=0,5
| 2 | 2,022 |
Video compression dataset and benchmark of learning-based video-quality metrics
| 4 |
neurips
| 0 | 0 |
2023-06-16 22:58:17.453000
|
https://github.com/msu-video-group/msu_vqm_compression_benchmark
| 16 |
Video compression dataset and benchmark of learning-based video-quality metrics
|
https://scholar.google.com/scholar?cluster=11117086154139094350&hl=en&as_sdt=0,47
| 3 | 2,022 |
Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection
| 1 |
neurips
| 2 | 0 |
2023-06-16 22:58:17.664000
|
https://github.com/cvmi-lab/fs3d
| 36 |
Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection
|
https://scholar.google.com/scholar?cluster=15115934605186565266&hl=en&as_sdt=0,43
| 5 | 2,022 |
Efficient Dataset Distillation using Random Feature Approximation
| 11 |
neurips
| 1 | 1 |
2023-06-16 22:58:17.876000
|
https://github.com/yolky/rfad
| 23 |
Efficient dataset distillation using random feature approximation
|
https://scholar.google.com/scholar?cluster=12794285551052496052&hl=en&as_sdt=0,5
| 3 | 2,022 |
Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?
| 9 |
neurips
| 1 | 0 |
2023-06-16 22:58:18.087000
|
https://github.com/justkolesov/wasserstein1benchmark
| 17 |
Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?
|
https://scholar.google.com/scholar?cluster=168357485459111534&hl=en&as_sdt=0,18
| 2 | 2,022 |
PALBERT: Teaching ALBERT to Ponder
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:58:18.299000
|
https://github.com/tinkoff-ai/palbert
| 34 |
PALBERT: Teaching ALBERT to Ponder
|
https://scholar.google.com/scholar?cluster=13888821126915681625&hl=en&as_sdt=0,14
| 3 | 2,022 |
Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:58:18.512000
|
https://github.com/tipt0p/three_regimes_on_the_sphere
| 3 |
Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes
|
https://scholar.google.com/scholar?cluster=6680465751161236858&hl=en&as_sdt=0,31
| 1 | 2,022 |
Exploring the Whole Rashomon Set of Sparse Decision Trees
| 12 |
neurips
| 5 | 1 |
2023-06-16 22:58:18.724000
|
https://github.com/ubc-systopia/treeFarms
| 19 |
Exploring the whole rashomon set of sparse decision trees
|
https://scholar.google.com/scholar?cluster=8197518784888953073&hl=en&as_sdt=0,34
| 1 | 2,022 |
Graph Self-supervised Learning with Accurate Discrepancy Learning
| 6 |
neurips
| 2 | 1 |
2023-06-16 22:58:18.936000
|
https://github.com/dongkikim95/d-sla
| 12 |
Graph self-supervised learning with accurate discrepancy learning
|
https://scholar.google.com/scholar?cluster=6899266835558351745&hl=en&as_sdt=0,11
| 1 | 2,022 |
Multi-Scale Adaptive Network for Single Image Denoising
| 5 |
neurips
| 1 | 0 |
2023-06-16 22:58:19.153000
|
https://github.com/xlearning-scu/2022-neurips-msanet
| 2 |
Multi-Scale Adaptive Network for Single Image Denoising
|
https://scholar.google.com/scholar?cluster=12092498430345383404&hl=en&as_sdt=0,39
| 2 | 2,022 |
Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:58:19.364000
|
https://github.com/ttesileanu/bio-pcn
| 5 |
Constrained predictive coding as a biologically plausible model of the cortical hierarchy
|
https://scholar.google.com/scholar?cluster=11118175748957488346&hl=en&as_sdt=0,5
| 1 | 2,022 |
Near-Optimal Collaborative Learning in Bandits
| 5 |
neurips
| 0 | 0 |
2023-06-16 22:58:19.577000
|
https://github.com/clreda/near-optimal-federated
| 1 |
Near-optimal collaborative learning in bandits
|
https://scholar.google.com/scholar?cluster=11872427930011371643&hl=en&as_sdt=0,11
| 1 | 2,022 |
TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers
| 8 |
neurips
| 3 | 0 |
2023-06-16 22:58:19.788000
|
https://github.com/mlvlab/tokenmixup
| 39 |
Tokenmixup: Efficient attention-guided token-level data augmentation for transformers
|
https://scholar.google.com/scholar?cluster=3326108237146565481&hl=en&as_sdt=0,25
| 5 | 2,022 |
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models
| 24 |
neurips
| 12 | 2 |
2023-06-16 22:58:20
|
https://github.com/azshue/TPT
| 64 |
Test-time prompt tuning for zero-shot generalization in vision-language models
|
https://scholar.google.com/scholar?cluster=213109028691722316&hl=en&as_sdt=0,1
| 3 | 2,022 |
SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders
| 22 |
neurips
| 3 | 2 |
2023-06-16 22:58:20.214000
|
https://github.com/ucasligang/semmae
| 16 |
Semmae: Semantic-guided masking for learning masked autoencoders
|
https://scholar.google.com/scholar?cluster=16607040036096933653&hl=en&as_sdt=0,23
| 1 | 2,022 |
BiT: Robustly Binarized Multi-distilled Transformer
| 13 |
neurips
| 9 | 5 |
2023-06-16 22:58:20.437000
|
https://github.com/facebookresearch/bit
| 67 |
Bit: Robustly binarized multi-distilled transformer
|
https://scholar.google.com/scholar?cluster=1714008465250842352&hl=en&as_sdt=0,5
| 12 | 2,022 |
Knowledge-Aware Bayesian Deep Topic Model
| 6 |
neurips
| 2 | 1 |
2023-06-16 22:58:20.647000
|
https://github.com/wds2014/topickg
| 3 |
Knowledge-aware Bayesian deep topic model
|
https://scholar.google.com/scholar?cluster=2627842395179821875&hl=en&as_sdt=0,44
| 1 | 2,022 |
SelecMix: Debiased Learning by Contradicting-pair Sampling
| 1 |
neurips
| 1 | 0 |
2023-06-16 22:58:20.862000
|
https://github.com/bluemoon010/selecmix
| 7 |
SelecMix: Debiased Learning by Contradicting-pair Sampling
|
https://scholar.google.com/scholar?cluster=2915792353103786474&hl=en&as_sdt=0,5
| 2 | 2,022 |
P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting
| 19 |
neurips
| 9 | 3 |
2023-06-16 22:58:21.073000
|
https://github.com/wangzy22/P2P
| 99 |
P2p: Tuning pre-trained image models for point cloud analysis with point-to-pixel prompting
|
https://scholar.google.com/scholar?cluster=16387925596110304701&hl=en&as_sdt=0,44
| 8 | 2,022 |
Variational inference via Wasserstein gradient flows
| 18 |
neurips
| 0 | 0 |
2023-06-16 22:58:21.288000
|
https://github.com/marc-h-lambert/w-vi
| 4 |
Variational inference via Wasserstein gradient flows
|
https://scholar.google.com/scholar?cluster=6278239632923753494&hl=en&as_sdt=0,5
| 1 | 2,022 |
projUNN: efficient method for training deep networks with unitary matrices
| 5 |
neurips
| 4 | 2 |
2023-06-16 22:58:21.500000
|
https://github.com/facebookresearch/projunn
| 20 |
projUNN: efficient method for training deep networks with unitary matrices
|
https://scholar.google.com/scholar?cluster=1850320121010807682&hl=en&as_sdt=0,5
| 49 | 2,022 |
Multi-dataset Training of Transformers for Robust Action Recognition
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:58:21.711000
|
https://github.com/junweiliang/multitrain
| 9 |
Multi-dataset Training of Transformers for Robust Action Recognition
|
https://scholar.google.com/scholar?cluster=18278928779930263666&hl=en&as_sdt=0,31
| 5 | 2,022 |
Recipe for a General, Powerful, Scalable Graph Transformer
| 62 |
neurips
| 63 | 5 |
2023-06-16 22:58:21.922000
|
https://github.com/rampasek/GraphGPS
| 390 |
Recipe for a general, powerful, scalable graph transformer
|
https://scholar.google.com/scholar?cluster=6992910764828744943&hl=en&as_sdt=0,33
| 11 | 2,022 |
Rare Gems: Finding Lottery Tickets at Initialization
| 10 |
neurips
| 2 | 9 |
2023-06-16 22:58:22.134000
|
https://github.com/ksreenivasan/pruning_is_enough
| 8 |
Rare gems: Finding lottery tickets at initialization
|
https://scholar.google.com/scholar?cluster=18354752168208884490&hl=en&as_sdt=0,14
| 4 | 2,022 |
Online Bipartite Matching with Advice: Tight Robustness-Consistency Tradeoffs for the Two-Stage Model
| 4 |
neurips
| 0 | 0 |
2023-06-16 22:58:22.345000
|
https://github.com/mapleox/matching_predictions
| 1 |
Online Bipartite Matching with Advice: Tight Robustness-Consistency Tradeoffs for the Two-Stage Model
|
https://scholar.google.com/scholar?cluster=10540192598939165742&hl=en&as_sdt=0,14
| 1 | 2,022 |
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials
| 4 |
neurips
| 0 | 0 |
2023-06-16 22:58:22.556000
|
https://github.com/eshnich/escape_ntk
| 0 |
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials
|
https://scholar.google.com/scholar?cluster=9098044485141039309&hl=en&as_sdt=0,36
| 1 | 2,022 |
Pure Transformers are Powerful Graph Learners
| 20 |
neurips
| 35 | 8 |
2023-06-16 22:58:22.766000
|
https://github.com/jw9730/tokengt
| 226 |
Pure transformers are powerful graph learners
|
https://scholar.google.com/scholar?cluster=1854387804616571098&hl=en&as_sdt=0,5
| 10 | 2,022 |
NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation
| 0 |
neurips
| 2 | 0 |
2023-06-16 22:58:22.978000
|
https://github.com/jeremiemelo/neurolight
| 23 |
NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation
|
https://scholar.google.com/scholar?cluster=8881238430961631710&hl=en&as_sdt=0,5
| 5 | 2,022 |
Learning the Structure of Large Networked Systems Obeying Conservation Laws
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:58:23.190000
|
https://github.com/anirudhrayas/slnscl
| 0 |
Learning the Structure of Large Networked Systems Obeying Conservation Laws
|
https://scholar.google.com/scholar?cluster=5489652265848095626&hl=en&as_sdt=0,5
| 1 | 2,022 |
Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets
| 2 |
neurips
| 3 | 0 |
2023-06-16 22:58:23.409000
|
https://github.com/arieseirack/dhvt
| 41 |
Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets
|
https://scholar.google.com/scholar?cluster=10766475797615971517&hl=en&as_sdt=0,14
| 3 | 2,022 |
Private Set Generation with Discriminative Information
| 11 |
neurips
| 0 | 2 |
2023-06-16 22:58:23.619000
|
https://github.com/dingfanchen/private-set
| 13 |
Private set generation with discriminative information
|
https://scholar.google.com/scholar?cluster=1058785882009175393&hl=en&as_sdt=0,44
| 1 | 2,022 |
Provable Defense against Backdoor Policies in Reinforcement Learning
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:58:23.830000
|
https://github.com/skbharti/provable-defense-in-rl
| 4 |
Provable Defense against Backdoor Policies in Reinforcement Learning
|
https://scholar.google.com/scholar?cluster=15582632130939406311&hl=en&as_sdt=0,5
| 1 | 2,022 |
Diffusion Models as Plug-and-Play Priors
| 32 |
neurips
| 10 | 3 |
2023-06-16 22:58:24.042000
|
https://github.com/alexgraikos/diffusion_priors
| 134 |
Diffusion models as plug-and-play priors
|
https://scholar.google.com/scholar?cluster=1664893972448348110&hl=en&as_sdt=0,47
| 3 | 2,022 |
VaiPhy: a Variational Inference Based Algorithm for Phylogeny
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:58:24.253000
|
https://github.com/lagergren-lab/vaiphy
| 1 |
VaiPhy: a Variational Inference Based Algorithm for Phylogeny
|
https://scholar.google.com/scholar?cluster=8569696227907853831&hl=en&as_sdt=0,5
| 1 | 2,022 |
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal
| 9 |
neurips
| 2 | 0 |
2023-06-16 22:58:24.465000
|
https://github.com/yaqianzhang/repeatedaugmentedrehearsal
| 6 |
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal
|
https://scholar.google.com/scholar?cluster=9507643277060053536&hl=en&as_sdt=0,48
| 2 | 2,022 |
Compressible-composable NeRF via Rank-residual Decomposition
| 23 |
neurips
| 10 | 4 |
2023-06-16 22:58:24.675000
|
https://github.com/ashawkey/ccnerf
| 116 |
Compressible-composable nerf via rank-residual decomposition
|
https://scholar.google.com/scholar?cluster=15357102335001383949&hl=en&as_sdt=0,5
| 11 | 2,022 |
Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:58:24.886000
|
https://github.com/shuix007/eip4nnpotentials
| 1 |
Injecting domain knowledge from empirical interatomic potentials to neural networks for predicting material properties
|
https://scholar.google.com/scholar?cluster=1090911456582952021&hl=en&as_sdt=0,10
| 3 | 2,022 |
Learning Modular Simulations for Homogeneous Systems
| 0 |
neurips
| 1 | 0 |
2023-06-16 22:58:25.097000
|
https://github.com/microsoft/mpnode.jl
| 29 |
Learning Modular Simulations for Homogeneous Systems
|
https://scholar.google.com/scholar?cluster=16943302604921582247&hl=en&as_sdt=0,48
| 4 | 2,022 |
Semi-Discrete Normalizing Flows through Differentiable Tessellation
| 2 |
neurips
| 1 | 0 |
2023-06-16 22:58:25.308000
|
https://github.com/facebookresearch/semi-discrete-flow
| 20 |
Semi-Discrete Normalizing Flows through Differentiable Tessellation
|
https://scholar.google.com/scholar?cluster=2894615893347018628&hl=en&as_sdt=0,39
| 3 | 2,022 |
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks
| 5 |
neurips
| 0 | 1 |
2023-06-16 22:58:25.519000
|
https://github.com/sizhe-chen/aaa
| 13 |
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks
|
https://scholar.google.com/scholar?cluster=1904818914099445692&hl=en&as_sdt=0,21
| 1 | 2,022 |
Sequence-to-Set Generative Models
| 0 |
neurips
| 2 | 0 |
2023-06-16 22:58:25.731000
|
https://github.com/longtaotang/setlearning
| 1 |
Sequence-to-Set Generative Models
|
https://scholar.google.com/scholar?cluster=11832911442532697900&hl=en&as_sdt=0,5
| 1 | 2,022 |
Near-Optimal Multi-Agent Learning for Safe Coverage Control
| 1 |
neurips
| 1 | 0 |
2023-06-16 22:58:25.943000
|
https://github.com/manish-pra/safemac
| 7 |
Near-Optimal Multi-Agent Learning for Safe Coverage Control
|
https://scholar.google.com/scholar?cluster=9831092712630856956&hl=en&as_sdt=0,33
| 2 | 2,022 |
Beyond spectral gap: the role of the topology in decentralized learning
| 6 |
neurips
| 0 | 0 |
2023-06-16 22:58:26.155000
|
https://github.com/epfml/topology-in-decentralized-learning
| 6 |
Beyond spectral gap: The role of the topology in decentralized learning
|
https://scholar.google.com/scholar?cluster=1362974330315569640&hl=en&as_sdt=0,44
| 3 | 2,022 |
Periodic Graph Transformers for Crystal Material Property Prediction
| 11 |
neurips
| 3 | 1 |
2023-06-16 22:58:26.366000
|
https://github.com/YKQ98/Matformer
| 47 |
Periodic Graph Transformers for Crystal Material Property Prediction
|
https://scholar.google.com/scholar?cluster=9619404030822952789&hl=en&as_sdt=0,38
| 5 | 2,022 |
Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
| 6 |
neurips
| 5 | 2 |
2023-06-16 22:58:26.579000
|
https://github.com/xiaoachen98/DDB
| 52 |
Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
|
https://scholar.google.com/scholar?cluster=12908675739985569858&hl=en&as_sdt=0,5
| 3 | 2,022 |
DreamShard: Generalizable Embedding Table Placement for Recommender Systems
| 9 |
neurips
| 1 | 0 |
2023-06-16 22:58:26.790000
|
https://github.com/daochenzha/dreamshard
| 26 |
Dreamshard: Generalizable embedding table placement for recommender systems
|
https://scholar.google.com/scholar?cluster=5762579680936509835&hl=en&as_sdt=0,5
| 3 | 2,022 |
Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE
| 2 |
neurips
| 2 | 0 |
2023-06-16 22:58:27.001000
|
https://github.com/smlc-nysbc/target-vae
| 13 |
Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE
|
https://scholar.google.com/scholar?cluster=4643268267251719909&hl=en&as_sdt=0,33
| 3 | 2,022 |
PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points
| 2 |
neurips
| 1 | 2 |
2023-06-16 22:58:27.213000
|
https://github.com/mcg-nju/pointtad
| 31 |
Pointtad: Multi-label temporal action detection with learnable query points
|
https://scholar.google.com/scholar?cluster=4239613475999349516&hl=en&as_sdt=0,33
| 3 | 2,022 |
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:58:27.424000
|
https://github.com/Stalence/NeuralExt
| 4 |
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
|
https://scholar.google.com/scholar?cluster=11142300575635398098&hl=en&as_sdt=0,5
| 1 | 2,022 |
Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification
| 4 |
neurips
| 10 | 1 |
2023-06-16 22:58:27.635000
|
https://github.com/miccaiif/weno
| 34 |
Bi-directional weakly supervised knowledge distillation for whole slide image classification
|
https://scholar.google.com/scholar?cluster=8347896172205638655&hl=en&as_sdt=0,36
| 3 | 2,022 |
PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient
| 4 |
neurips
| 181 | 91 |
2023-06-16 22:58:27.846000
|
https://github.com/open-mmlab/mmrazor
| 1,088 |
PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient
|
https://scholar.google.com/scholar?cluster=15197137746726757661&hl=en&as_sdt=0,10
| 19 | 2,022 |
NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis
| 17 |
neurips
| 153 | 14 |
2023-06-16 22:58:28.057000
|
https://github.com/microsoft/nuwa
| 2,707 |
Nuwa-infinity: Autoregressive over autoregressive generation for infinite visual synthesis
|
https://scholar.google.com/scholar?cluster=13240374514444074345&hl=en&as_sdt=0,7
| 143 | 2,022 |
Stability Analysis and Generalization Bounds of Adversarial Training
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:58:28.268000
|
https://github.com/JiancongXiao/Stability-of-Adversarial-Training
| 2 |
Stability analysis and generalization bounds of adversarial training
|
https://scholar.google.com/scholar?cluster=4247121934226238783&hl=en&as_sdt=0,33
| 1 | 2,022 |
STaR: Bootstrapping Reasoning With Reasoning
| 85 |
neurips
| 6 | 0 |
2023-06-16 22:58:28.484000
|
https://github.com/ezelikman/STaR
| 20 |
Star: Bootstrapping reasoning with reasoning
|
https://scholar.google.com/scholar?cluster=6588800596180274414&hl=en&as_sdt=0,14
| 1 | 2,022 |
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation
| 2 |
neurips
| 1 | 0 |
2023-06-16 22:58:28.695000
|
https://github.com/stilwell-git/adaptation-with-noisy-oracle
| 3 |
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation
|
https://scholar.google.com/scholar?cluster=12503746065360790746&hl=en&as_sdt=0,5
| 2 | 2,022 |
Weakly Supervised Representation Learning with Sparse Perturbations
| 11 |
neurips
| 0 | 0 |
2023-06-16 22:58:28.906000
|
https://github.com/ahujak/wsrl
| 0 |
Weakly supervised representation learning with sparse perturbations
|
https://scholar.google.com/scholar?cluster=5928274395682008683&hl=en&as_sdt=0,41
| 1 | 2,022 |
Watermarking for Out-of-distribution Detection
| 4 |
neurips
| 2 | 0 |
2023-06-16 22:58:29.117000
|
https://github.com/qizhouwang/watermarking
| 10 |
Watermarking for Out-of-distribution Detection
|
https://scholar.google.com/scholar?cluster=14042029283291490588&hl=en&as_sdt=0,33
| 1 | 2,022 |
EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records
| 2 |
neurips
| 9 | 0 |
2023-06-16 22:58:29.329000
|
https://github.com/glee4810/EHRSQL
| 36 |
EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records
|
https://scholar.google.com/scholar?cluster=8956258088205666681&hl=en&as_sdt=0,23
| 3 | 2,022 |
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability
| 2 |
neurips
| 1 | 0 |
2023-06-16 22:58:29.540000
|
https://github.com/LevinRoman/parameter-space-saliency
| 21 |
Where do Models go wrong? Parameter-space saliency maps for explainability
|
https://scholar.google.com/scholar?cluster=6375709581845585510&hl=en&as_sdt=0,5
| 2 | 2,022 |
Using Embeddings for Causal Estimation of Peer Influence in Social Networks
| 2 |
neurips
| 3 | 0 |
2023-06-16 22:58:29.751000
|
https://github.com/irinacristali/peer-contagion-on-networks
| 6 |
Using embeddings for causal estimation of peer influence in social networks
|
https://scholar.google.com/scholar?cluster=10956063829097823219&hl=en&as_sdt=0,15
| 1 | 2,022 |
Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?
| 5 |
neurips
| 1 | 0 |
2023-06-16 22:58:29.962000
|
https://github.com/dendorferpatrick/quovadis
| 19 |
Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?
|
https://scholar.google.com/scholar?cluster=17768927827009981298&hl=en&as_sdt=0,14
| 3 | 2,022 |
Wasserstein Iterative Networks for Barycenter Estimation
| 11 |
neurips
| 0 | 1 |
2023-06-16 22:58:30.174000
|
https://github.com/iamalexkorotin/wassersteiniterativenetworks
| 3 |
Wasserstein iterative networks for barycenter estimation
|
https://scholar.google.com/scholar?cluster=6505548225666677645&hl=en&as_sdt=0,33
| 2 | 2,022 |
OpenXAI: Towards a Transparent Evaluation of Model Explanations
| 14 |
neurips
| 21 | 4 |
2023-06-16 22:58:30.402000
|
https://github.com/ai4life-group/openxai
| 158 |
Openxai: Towards a transparent evaluation of model explanations
|
https://scholar.google.com/scholar?cluster=1602716306137073411&hl=en&as_sdt=0,15
| 6 | 2,022 |
The Hessian Screening Rule
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:58:30.614000
|
https://github.com/jolars/HessianScreening
| 2 |
The hessian screening rule
|
https://scholar.google.com/scholar?cluster=4519092645139921267&hl=en&as_sdt=0,5
| 3 | 2,022 |
Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
| 6 |
neurips
| 0 | 0 |
2023-06-16 22:58:30.825000
|
https://github.com/totilas/muffliato
| 0 |
Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
|
https://scholar.google.com/scholar?cluster=1367771846266948746&hl=en&as_sdt=0,5
| 1 | 2,022 |
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment
| 5 |
neurips
| 0 | 0 |
2023-06-16 22:58:31.036000
|
https://github.com/causalml/boundsonfractionnegativelyaffected
| 1 |
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment
|
https://scholar.google.com/scholar?cluster=15108195108201398305&hl=en&as_sdt=0,33
| 0 | 2,022 |
Training Subset Selection for Weak Supervision
| 7 |
neurips
| 1 | 0 |
2023-06-16 22:58:31.247000
|
https://github.com/hunterlang/weaksup-subset-selection
| 11 |
Training Subset Selection for Weak Supervision
|
https://scholar.google.com/scholar?cluster=8350401146899292084&hl=en&as_sdt=0,33
| 1 | 2,022 |
Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation
| 7 |
neurips
| 0 | 1 |
2023-06-16 22:58:31.460000
|
https://github.com/tyroneli/esol_wsss
| 13 |
Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation
|
https://scholar.google.com/scholar?cluster=7949251840753978462&hl=en&as_sdt=0,14
| 4 | 2,022 |
RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning
| 23 |
neurips
| 5 | 0 |
2023-06-16 22:58:31.671000
|
https://github.com/marc-rigter/rambo
| 13 |
Rambo-rl: Robust adversarial model-based offline reinforcement learning
|
https://scholar.google.com/scholar?cluster=10956894200939947900&hl=en&as_sdt=0,5
| 3 | 2,022 |
Improved techniques for deterministic l2 robustness
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:58:31.882000
|
https://github.com/singlasahil14/improved_l2_robustness
| 2 |
Improved techniques for deterministic l2 robustness
|
https://scholar.google.com/scholar?cluster=7826478224730238594&hl=en&as_sdt=0,5
| 1 | 2,022 |
Normalizing Flows for Knockoff-free Controlled Feature Selection
| 1 |
neurips
| 2 | 1 |
2023-06-16 22:58:32.093000
|
https://github.com/dereklhansen/flowselect
| 6 |
Normalizing flows for knockoff-free controlled feature selection
|
https://scholar.google.com/scholar?cluster=1427873937634321585&hl=en&as_sdt=0,5
| 1 | 2,022 |
Efficient Architecture Search for Diverse Tasks
| 5 |
neurips
| 3 | 0 |
2023-06-16 22:58:32.305000
|
https://github.com/sjunhongshen/dash
| 20 |
Efficient architecture search for diverse tasks
|
https://scholar.google.com/scholar?cluster=6159039417231853231&hl=en&as_sdt=0,39
| 1 | 2,022 |
Inherently Explainable Reinforcement Learning in Natural Language
| 4 |
neurips
| 0 | 0 |
2023-06-16 22:58:32.516000
|
https://github.com/xiangyu-peng/hex-rl
| 5 |
Inherently explainable reinforcement learning in natural language
|
https://scholar.google.com/scholar?cluster=14816477869397516232&hl=en&as_sdt=0,10
| 1 | 2,022 |
On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting
| 7 |
neurips
| 21 | 0 |
2023-06-16 22:58:32.727000
|
https://github.com/naver/gdc
| 108 |
On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting
|
https://scholar.google.com/scholar?cluster=852205239586657946&hl=en&as_sdt=0,51
| 10 | 2,022 |
Ask4Help: Learning to Leverage an Expert for Embodied Tasks
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:58:32.939000
|
https://github.com/allenai/ask4help
| 17 |
Ask4help: Learning to leverage an expert for embodied tasks
|
https://scholar.google.com/scholar?cluster=893074409326064845&hl=en&as_sdt=0,33
| 3 | 2,022 |
Active Bayesian Causal Inference
| 7 |
neurips
| 2 | 0 |
2023-06-16 22:58:33.150000
|
https://github.com/chritoth/active-bayesian-causal-inference
| 21 |
Active Bayesian Causal Inference
|
https://scholar.google.com/scholar?cluster=14185975867772832007&hl=en&as_sdt=0,5
| 2 | 2,022 |
LogiGAN: Learning Logical Reasoning via Adversarial Pre-training
| 3 |
neurips
| 58 | 10 |
2023-06-16 22:58:33.361000
|
https://github.com/microsoft/ContextualSP
| 310 |
Logigan: Learning logical reasoning via adversarial pre-training
|
https://scholar.google.com/scholar?cluster=16806536241461518439&hl=en&as_sdt=0,5
| 15 | 2,022 |
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
| 88 |
neurips
| 308 | 111 |
2023-06-16 22:58:33.573000
|
https://github.com/hazyresearch/flash-attention
| 3,654 |
Flashattention: Fast and memory-efficient exact attention with io-awareness
|
https://scholar.google.com/scholar?cluster=4436654227589737701&hl=en&as_sdt=0,5
| 67 | 2,022 |
Self-Supervised Visual Representation Learning with Semantic Grouping
| 14 |
neurips
| 6 | 4 |
2023-06-16 22:58:33.784000
|
https://github.com/CVMI-Lab/SlotCon
| 76 |
Self-supervised visual representation learning with semantic grouping
|
https://scholar.google.com/scholar?cluster=11920603760559197380&hl=en&as_sdt=0,5
| 3 | 2,022 |
Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds
| 8 |
neurips
| 2 | 0 |
2023-06-16 22:58:33.995000
|
https://github.com/junshengzhou/cap-udf
| 37 |
Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds
|
https://scholar.google.com/scholar?cluster=3947486102565885083&hl=en&as_sdt=0,47
| 3 | 2,022 |
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem
| 26 |
neurips
| 26 | 4 |
2023-06-16 22:58:34.209000
|
https://github.com/pku-marl/multi-agent-transformer
| 147 |
Multi-agent reinforcement learning is a sequence modeling problem
|
https://scholar.google.com/scholar?cluster=14170076594522259195&hl=en&as_sdt=0,39
| 7 | 2,022 |
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
| 6 |
neurips
| 1 | 0 |
2023-06-16 22:58:34.420000
|
https://github.com/ma921/basq
| 11 |
Fast Bayesian inference with batch Bayesian quadrature via kernel recombination
|
https://scholar.google.com/scholar?cluster=9942624906464459479&hl=en&as_sdt=0,14
| 1 | 2,022 |
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:58:34.632000
|
https://github.com/mlohaus/disparatetreatment
| 0 |
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks
|
https://scholar.google.com/scholar?cluster=8811649943714147381&hl=en&as_sdt=0,5
| 1 | 2,022 |
Fast Instrument Learning with Faster Rates
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:58:34.843000
|
https://github.com/meta-inf/fil
| 0 |
Fast Instrument Learning with Faster Rates
|
https://scholar.google.com/scholar?cluster=6761597304576361829&hl=en&as_sdt=0,31
| 1 | 2,022 |
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
| 62 |
neurips
| 13 | 12 |
2023-06-16 22:58:35.055000
|
https://github.com/ShoufaChen/AdaptFormer
| 194 |
Adaptformer: Adapting vision transformers for scalable visual recognition
|
https://scholar.google.com/scholar?cluster=17752815312316743733&hl=en&as_sdt=0,47
| 6 | 2,022 |
Symmetry Teleportation for Accelerated Optimization
| 2 |
neurips
| 3 | 0 |
2023-06-16 22:58:35.266000
|
https://github.com/rose-stl-lab/symmetry-teleportation
| 6 |
Symmetry Teleportation for Accelerated Optimization
|
https://scholar.google.com/scholar?cluster=1373110452926814805&hl=en&as_sdt=0,5
| 2 | 2,022 |
Wasserstein Logistic Regression with Mixed Features
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:58:35.477000
|
https://github.com/selvi-aras/wassersteinlr
| 3 |
Wasserstein logistic regression with mixed features
|
https://scholar.google.com/scholar?cluster=7859002643668729721&hl=en&as_sdt=0,6
| 3 | 2,022 |
Trajectory Inference via Mean-field Langevin in Path Space
| 5 |
neurips
| 0 | 0 |
2023-06-16 22:58:35.689000
|
https://github.com/zsteve/mfl
| 1 |
Trajectory inference via mean-field Langevin in path space
|
https://scholar.google.com/scholar?cluster=14010724729856799724&hl=en&as_sdt=0,33
| 1 | 2,022 |
SwinTrack: A Simple and Strong Baseline for Transformer Tracking
| 79 |
neurips
| 37 | 25 |
2023-06-16 22:58:35.902000
|
https://github.com/litinglin/swintrack
| 213 |
Swintrack: A simple and strong baseline for transformer tracking
|
https://scholar.google.com/scholar?cluster=6278077695056066484&hl=en&as_sdt=0,44
| 5 | 2,022 |
Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation
| 3 |
neurips
| 0 | 2 |
2023-06-16 22:58:36.115000
|
https://github.com/joonho-jang/uadal
| 8 |
Unknown-aware domain adversarial learning for open-set domain adaptation
|
https://scholar.google.com/scholar?cluster=17997080445903067240&hl=en&as_sdt=0,33
| 2 | 2,022 |
Poisson Flow Generative Models
| 17 |
neurips
| 60 | 3 |
2023-06-16 22:58:36.326000
|
https://github.com/newbeeer/poisson_flow
| 747 |
Poisson flow generative models
|
https://scholar.google.com/scholar?cluster=14573129279323287718&hl=en&as_sdt=0,5
| 15 | 2,022 |
Invertible Monotone Operators for Normalizing Flows
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:58:36.538000
|
https://github.com/mlvlab/monotoneflows
| 7 |
Invertible Monotone Operators for Normalizing Flows
|
https://scholar.google.com/scholar?cluster=9497056797525394758&hl=en&as_sdt=0,5
| 3 | 2,022 |
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