title
stringlengths 8
155
| citations_google_scholar
int64 0
28.9k
| conference
stringclasses 5
values | forks
int64 0
46.3k
| issues
int64 0
12.2k
| lastModified
stringlengths 19
26
| repo_url
stringlengths 26
130
| stars
int64 0
75.9k
| title_google_scholar
stringlengths 8
155
| url_google_scholar
stringlengths 75
206
| watchers
int64 0
2.77k
| year
int64 2.02k
2.02k
|
|---|---|---|---|---|---|---|---|---|---|---|---|
Dataset Distillation using Neural Feature Regression
| 24 |
neurips
| 7 | 1 |
2023-06-16 22:57:54.176000
|
https://github.com/yongchao97/FRePo
| 28 |
Dataset distillation using neural feature regression
|
https://scholar.google.com/scholar?cluster=15355176449784124932&hl=en&as_sdt=0,33
| 3 | 2,022 |
ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time
| 7 |
neurips
| 2 | 0 |
2023-06-16 22:57:54.403000
|
https://github.com/snap-stanford/zeroc
| 19 |
Zeroc: A neuro-symbolic model for zero-shot concept recognition and acquisition at inference time
|
https://scholar.google.com/scholar?cluster=3612242931318475489&hl=en&as_sdt=0,22
| 44 | 2,022 |
Risk-Driven Design of Perception Systems
| 0 |
neurips
| 1 | 0 |
2023-06-16 22:57:54.614000
|
https://github.com/sisl/riskdrivenperception
| 3 |
Risk-Driven Design of Perception Systems
|
https://scholar.google.com/scholar?cluster=3006168152104696613&hl=en&as_sdt=0,5
| 4 | 2,022 |
A Simple Approach to Automated Spectral Clustering
| 2 |
neurips
| 0 | 1 |
2023-06-16 22:57:54.825000
|
https://github.com/jicongfan/automated-spectral-clustering
| 2 |
A simple approach to automated spectral clustering
|
https://scholar.google.com/scholar?cluster=2848547418778533477&hl=en&as_sdt=0,36
| 1 | 2,022 |
Joint Entropy Search for Multi-Objective Bayesian Optimization
| 6 |
neurips
| 1 | 0 |
2023-06-16 22:57:55.036000
|
https://github.com/benmltu/jes
| 12 |
Joint entropy search for multi-objective bayesian optimization
|
https://scholar.google.com/scholar?cluster=15207167627489331903&hl=en&as_sdt=0,19
| 1 | 2,022 |
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation
| 1 |
neurips
| 0 | 1 |
2023-06-16 22:57:55.248000
|
https://github.com/jpgard/subgroup-robustness-grows-on-trees
| 1 |
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation
|
https://scholar.google.com/scholar?cluster=1553255834314203495&hl=en&as_sdt=0,47
| 1 | 2,022 |
LION: Latent Point Diffusion Models for 3D Shape Generation
| 62 |
neurips
| 32 | 12 |
2023-06-16 22:57:55.460000
|
https://github.com/nv-tlabs/LION
| 548 |
LION: Latent point diffusion models for 3D shape generation
|
https://scholar.google.com/scholar?cluster=11609382506929684644&hl=en&as_sdt=0,11
| 44 | 2,022 |
MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples
| 1 |
neurips
| 1 | 0 |
2023-06-16 22:57:55.672000
|
https://github.com/quwenjie/multiguard
| 2 |
MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples
|
https://scholar.google.com/scholar?cluster=16148192613023633792&hl=en&as_sdt=0,22
| 1 | 2,022 |
On Measuring Excess Capacity in Neural Networks
| 4 |
neurips
| 0 | 0 |
2023-06-16 22:57:55.883000
|
https://github.com/rkwitt/excess_capacity
| 0 |
On measuring excess capacity in neural networks
|
https://scholar.google.com/scholar?cluster=8286514853614308295&hl=en&as_sdt=0,23
| 2 | 2,022 |
Parameter-Efficient Masking Networks
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:57:56.094000
|
https://github.com/yueb17/pemn
| 14 |
Parameter-Efficient Masking Networks
|
https://scholar.google.com/scholar?cluster=3375567812720133580&hl=en&as_sdt=0,5
| 2 | 2,022 |
End-to-end Symbolic Regression with Transformers
| 32 |
neurips
| 6 | 4 |
2023-06-16 22:57:56.305000
|
https://github.com/facebookresearch/symbolicregression
| 39 |
End-to-end symbolic regression with transformers
|
https://scholar.google.com/scholar?cluster=13569402473810241669&hl=en&as_sdt=0,44
| 4 | 2,022 |
EcoFormer: Energy-Saving Attention with Linear Complexity
| 8 |
neurips
| 1 | 1 |
2023-06-16 22:57:56.516000
|
https://github.com/ziplab/ecoformer
| 60 |
Ecoformer: Energy-saving attention with linear complexity
|
https://scholar.google.com/scholar?cluster=12196003903025483137&hl=en&as_sdt=0,48
| 5 | 2,022 |
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time
| 13 |
neurips
| 7 | 6 |
2023-06-16 22:57:56.727000
|
https://github.com/huaxiuyao/wild-time
| 49 |
Wild-time: A benchmark of in-the-wild distribution shift over time
|
https://scholar.google.com/scholar?cluster=12470744137018985399&hl=en&as_sdt=0,44
| 3 | 2,022 |
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions
| 57 |
neurips
| 35 | 2 |
2023-06-16 22:57:56.939000
|
https://github.com/raoyongming/hornet
| 277 |
Hornet: Efficient high-order spatial interactions with recursive gated convolutions
|
https://scholar.google.com/scholar?cluster=12938213222665733645&hl=en&as_sdt=0,44
| 4 | 2,022 |
Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:57:57.150000
|
https://github.com/tsuchhiii/fixed-budget-bai
| 1 |
Minimax optimal algorithms for fixed-budget best arm identification
|
https://scholar.google.com/scholar?cluster=2208314113749435910&hl=en&as_sdt=0,34
| 1 | 2,022 |
Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching
| 0 |
neurips
| 2 | 0 |
2023-06-16 22:57:57.360000
|
https://github.com/sentient07/deformationbasis
| 9 |
Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching
|
https://scholar.google.com/scholar?cluster=6711357441182968462&hl=en&as_sdt=0,10
| 3 | 2,022 |
Automatic Differentiation of Programs with Discrete Randomness
| 4 |
neurips
| 8 | 10 |
2023-06-16 22:57:57.571000
|
https://github.com/gaurav-arya/stochasticad.jl
| 144 |
Automatic differentiation of programs with discrete randomness
|
https://scholar.google.com/scholar?cluster=4520468158435418424&hl=en&as_sdt=0,5
| 4 | 2,022 |
NS3: Neuro-symbolic Semantic Code Search
| 4 |
neurips
| 0 | 0 |
2023-06-16 22:57:57.782000
|
https://github.com/shushanarakelyan/modular_code_search
| 3 |
NS3: Neuro-symbolic semantic code search
|
https://scholar.google.com/scholar?cluster=12732470567380886921&hl=en&as_sdt=0,5
| 1 | 2,022 |
Revisiting Sparse Convolutional Model for Visual Recognition
| 2 |
neurips
| 6 | 2 |
2023-06-16 22:57:57.994000
|
https://github.com/delay-xili/sdnet
| 111 |
Revisiting sparse convolutional model for visual recognition
|
https://scholar.google.com/scholar?cluster=7681982241768438501&hl=en&as_sdt=0,7
| 9 | 2,022 |
BackdoorBench: A Comprehensive Benchmark of Backdoor Learning
| 28 |
neurips
| 24 | 4 |
2023-06-16 22:57:58.206000
|
https://github.com/sclbd/backdoorbench
| 162 |
Backdoorbench: A comprehensive benchmark of backdoor learning
|
https://scholar.google.com/scholar?cluster=13477998480458836443&hl=en&as_sdt=0,5
| 3 | 2,022 |
REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering
| 12 |
neurips
| 1 | 0 |
2023-06-16 22:57:58.423000
|
https://github.com/yzleroy/revive
| 19 |
Revive: Regional visual representation matters in knowledge-based visual question answering
|
https://scholar.google.com/scholar?cluster=15826539500910476875&hl=en&as_sdt=0,33
| 8 | 2,022 |
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:57:58.634000
|
https://github.com/peidehuang/gradient
| 5 |
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation
|
https://scholar.google.com/scholar?cluster=13844074007413994501&hl=en&as_sdt=0,4
| 3 | 2,022 |
Symbolic Distillation for Learned TCP Congestion Control
| 0 |
neurips
| 0 | 1 |
2023-06-16 22:57:58.846000
|
https://github.com/vita-group/symbolicpcc
| 6 |
Symbolic Distillation for Learned TCP Congestion Control
|
https://scholar.google.com/scholar?cluster=13401562754080828114&hl=en&as_sdt=0,5
| 9 | 2,022 |
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
| 1 |
neurips
| 1 | 0 |
2023-06-16 22:57:59.056000
|
https://github.com/d-tiapkin/optimistic-psrl-experiments
| 0 |
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
|
https://scholar.google.com/scholar?cluster=12169857257765503180&hl=en&as_sdt=0,6
| 1 | 2,022 |
Can Push-forward Generative Models Fit Multimodal Distributions?
| 6 |
neurips
| 0 | 0 |
2023-06-16 22:57:59.278000
|
https://github.com/antoinesalmona/push-forward-generative-models
| 1 |
Can Push-forward Generative Models Fit Multimodal Distributions?
|
https://scholar.google.com/scholar?cluster=15185434637554418912&hl=en&as_sdt=0,5
| 1 | 2,022 |
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination
| 19 |
neurips
| 4 | 1 |
2023-06-16 22:57:59.493000
|
https://github.com/zyzisastudyreallyhardguy/graph-group-discrimination
| 43 |
Rethinking and scaling up graph contrastive learning: An extremely efficient approach with group discrimination
|
https://scholar.google.com/scholar?cluster=13490371651179732416&hl=en&as_sdt=0,5
| 2 | 2,022 |
Diverse Weight Averaging for Out-of-Distribution Generalization
| 21 |
neurips
| 5 | 0 |
2023-06-16 22:57:59.704000
|
https://github.com/alexrame/diwa
| 16 |
Diverse weight averaging for out-of-distribution generalization
|
https://scholar.google.com/scholar?cluster=5971058245242972538&hl=en&as_sdt=0,14
| 2 | 2,022 |
Posterior and Computational Uncertainty in Gaussian Processes
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:57:59.916000
|
https://github.com/jonathanwenger/itergp
| 24 |
Posterior and Computational Uncertainty in Gaussian Processes
|
https://scholar.google.com/scholar?cluster=10582501668199508293&hl=en&as_sdt=0,10
| 2 | 2,022 |
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning
| 0 |
neurips
| 1 | 0 |
2023-06-16 22:58:00.127000
|
https://github.com/setarehc/deep_rl_regions
| 1 |
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning
|
https://scholar.google.com/scholar?cluster=11513659328041109175&hl=en&as_sdt=0,34
| 1 | 2,022 |
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis
| 7 |
neurips
| 0 | 0 |
2023-06-16 22:58:00.338000
|
https://github.com/rflperry/sparse_shift
| 6 |
Causal discovery in heterogeneous environments under the sparse mechanism shift hypothesis
|
https://scholar.google.com/scholar?cluster=13871332689539937340&hl=en&as_sdt=0,23
| 2 | 2,022 |
Towards Practical Control of Singular Values of Convolutional Layers
| 2 |
neurips
| 1 | 0 |
2023-06-16 22:58:00.549000
|
https://github.com/whiteteadragon/practical_svd_conv
| 4 |
Towards practical control of singular values of convolutional layers
|
https://scholar.google.com/scholar?cluster=1506769972607423712&hl=en&as_sdt=0,14
| 2 | 2,022 |
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
| 12 |
neurips
| 9 | 3 |
2023-06-16 22:58:00.760000
|
https://github.com/xiaoaoran/polarmix
| 41 |
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
|
https://scholar.google.com/scholar?cluster=2359394852358979496&hl=en&as_sdt=0,5
| 6 | 2,022 |
Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees
| 1 |
neurips
| 1 | 3 |
2023-06-16 22:58:00.972000
|
https://github.com/jjbrophy47/ibug
| 20 |
Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees
|
https://scholar.google.com/scholar?cluster=4132673829192129135&hl=en&as_sdt=0,33
| 5 | 2,022 |
AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos
| 8 |
neurips
| 24 | 5 |
2023-06-16 22:58:01.183000
|
https://github.com/tencentarc/animesr
| 230 |
AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos
|
https://scholar.google.com/scholar?cluster=3700240933025394368&hl=en&as_sdt=0,19
| 17 | 2,022 |
Fairness Transferability Subject to Bounded Distribution Shift
| 13 |
neurips
| 0 | 0 |
2023-06-16 22:58:01.416000
|
https://github.com/ucsc-real/fairness_transferability
| 2 |
Fairness transferability subject to bounded distribution shift
|
https://scholar.google.com/scholar?cluster=15393835531531070174&hl=en&as_sdt=0,44
| 0 | 2,022 |
Improving Self-Supervised Learning by Characterizing Idealized Representations
| 11 |
neurips
| 3 | 1 |
2023-06-16 22:58:01.627000
|
https://github.com/yanndubs/invariant-self-supervised-learning
| 34 |
Improving self-supervised learning by characterizing idealized representations
|
https://scholar.google.com/scholar?cluster=6601803486555515746&hl=en&as_sdt=0,1
| 1 | 2,022 |
On the difficulty of learning chaotic dynamics with RNNs
| 4 |
neurips
| 0 | 0 |
2023-06-16 22:58:01.839000
|
https://github.com/durstewitzlab/chaosrnn
| 3 |
On the difficulty of learning chaotic dynamics with RNNs
|
https://scholar.google.com/scholar?cluster=1853395383421685801&hl=en&as_sdt=0,39
| 1 | 2,022 |
SKFlow: Learning Optical Flow with Super Kernels
| 10 |
neurips
| 1 | 0 |
2023-06-16 22:58:02.050000
|
https://github.com/littlespray/SKFlow
| 29 |
SKFlow: Learning Optical Flow with Super Kernels
|
https://scholar.google.com/scholar?cluster=5401118479575242953&hl=en&as_sdt=0,33
| 2 | 2,022 |
End-to-end Stochastic Optimization with Energy-based Model
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:58:02.261000
|
https://github.com/Lingkai-Kong/SO-EBM
| 8 |
End-to-End Stochastic Optimization with Energy-Based Model
|
https://scholar.google.com/scholar?cluster=7358543026013028300&hl=en&as_sdt=0,44
| 2 | 2,022 |
Wasserstein $K$-means for clustering probability distributions
| 6 |
neurips
| 0 | 0 |
2023-06-16 22:58:02.474000
|
https://github.com/yubo02/wasserstein-k-means-for-clustering-probability-distributions
| 6 |
Wasserstein -means for clustering probability distributions
|
https://scholar.googleusercontent.com/scholar?q=cache:S92oB8p7PW0J:scholar.google.com/+Wasserstein+%24K%24-means+for+clustering+probability+distributions&hl=en&as_sdt=0,5
| 1 | 2,022 |
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
| 44 |
neurips
| 50 | 22 |
2023-06-16 22:58:02.685000
|
https://github.com/ACEsuit/mace
| 161 |
MACE: Higher order equivariant message passing neural networks for fast and accurate force fields
|
https://scholar.google.com/scholar?cluster=14632576704960076515&hl=en&as_sdt=0,33
| 17 | 2,022 |
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
| 0 |
neurips
| 20 | 13 |
2023-06-16 22:58:02.896000
|
https://github.com/yuangh-x/2022-nips-tenrec
| 125 |
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
|
https://scholar.google.com/scholar?cluster=17000003816321898501&hl=en&as_sdt=0,50
| 2 | 2,022 |
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning
| 2 |
neurips
| 1 | 2 |
2023-06-16 22:58:03.107000
|
https://github.com/dsshim0125/s2p
| 2 |
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning
|
https://scholar.google.com/scholar?cluster=10891821671072337532&hl=en&as_sdt=0,6
| 1 | 2,022 |
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold
| 9 |
neurips
| 0 | 0 |
2023-06-16 22:58:03.318000
|
https://github.com/cjyaras/normalized-neural-collapse
| 1 |
Neural collapse with normalized features: A geometric analysis over the riemannian manifold
|
https://scholar.google.com/scholar?cluster=184799387067688052&hl=en&as_sdt=0,5
| 1 | 2,022 |
Conformalized Fairness via Quantile Regression
| 1 |
neurips
| 1 | 0 |
2023-06-16 22:58:03.529000
|
https://github.com/lei-ding07/conformal_quantile_fairness
| 4 |
Conformalized Fairness via Quantile Regression
|
https://scholar.google.com/scholar?cluster=13625755204473996808&hl=en&as_sdt=0,23
| 1 | 2,022 |
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:58:03.740000
|
https://github.com/leoiv/baxus
| 6 |
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
|
https://scholar.google.com/scholar?cluster=16209973749760389058&hl=en&as_sdt=0,5
| 1 | 2,022 |
Evolution of Neural Tangent Kernels under Benign and Adversarial Training
| 5 |
neurips
| 0 | 0 |
2023-06-16 22:58:03.951000
|
https://github.com/yolky/adversarial_ntk_evolution
| 3 |
Evolution of neural tangent kernels under benign and adversarial training
|
https://scholar.google.com/scholar?cluster=10465513161130337378&hl=en&as_sdt=0,34
| 1 | 2,022 |
Zero-Sum Stochastic Stackelberg Games
| 1 |
neurips
| 1 | 0 |
2023-06-16 22:58:04.162000
|
https://github.com/sadie-zhao/zero-sum-stochastic-stackelberg-games-neurips
| 9 |
Zero-Sum Stochastic Stackelberg Games
|
https://scholar.google.com/scholar?cluster=16214046013626830778&hl=en&as_sdt=0,5
| 2 | 2,022 |
Evaluating Out-of-Distribution Performance on Document Image Classifiers
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:58:04.402000
|
https://github.com/gxlarson/rvl-cdip-ood
| 3 |
Evaluating Out-of-Distribution Performance on Document Image Classifiers
|
https://scholar.google.com/scholar?cluster=6783517671736097904&hl=en&as_sdt=0,39
| 1 | 2,022 |
Spatial Mixture-of-Experts
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:58:04.613000
|
https://github.com/spcl/smoe
| 6 |
Spatial Mixture-of-Experts
|
https://scholar.google.com/scholar?cluster=14828485216186842836&hl=en&as_sdt=0,44
| 7 | 2,022 |
Hilbert Distillation for Cross-Dimensionality Networks
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:58:04.824000
|
https://github.com/EagleMIT/Hilbert-Distillation
| 2 |
Hilbert Distillation for Cross-Dimensionality Networks
|
https://scholar.google.com/scholar?cluster=12406023256328088160&hl=en&as_sdt=0,14
| 1 | 2,022 |
LIFT: Language-Interfaced Fine-Tuning for Non-language Machine Learning Tasks
| 13 |
neurips
| 6 | 4 |
2023-06-16 22:58:05.035000
|
https://github.com/uw-madison-lee-lab/languageinterfacedfinetuning
| 74 |
Lift: Language-interfaced fine-tuning for non-language machine learning tasks
|
https://scholar.google.com/scholar?cluster=15884163467852791519&hl=en&as_sdt=0,44
| 5 | 2,022 |
Template based Graph Neural Network with Optimal Transport Distances
| 2 |
neurips
| 0 | 2 |
2023-06-16 22:58:05.247000
|
https://github.com/cedricvincentcuaz/TFGW
| 1 |
Template based graph neural network with optimal transport distances
|
https://scholar.google.com/scholar?cluster=10849560620712329134&hl=en&as_sdt=0,5
| 2 | 2,022 |
GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations
| 2 |
neurips
| 1 | 0 |
2023-06-16 22:58:05.458000
|
https://github.com/dem123456789/gal-gradient-assisted-learning-for-decentralized-multi-organization-collaborations
| 5 |
GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations
|
https://scholar.google.com/scholar?cluster=14405130347379679970&hl=en&as_sdt=0,5
| 2 | 2,022 |
Direct Advantage Estimation
| 3 |
neurips
| 1 | 0 |
2023-06-16 22:58:05.670000
|
https://github.com/hrpan/dae
| 4 |
Direct advantage estimation
|
https://scholar.google.com/scholar?cluster=723226367131333982&hl=en&as_sdt=0,19
| 3 | 2,022 |
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning
| 5 |
neurips
| 58 | 8 |
2023-06-16 22:58:05.880000
|
https://github.com/tencent-ailab/hok_env
| 438 |
Honor of kings arena: an environment for generalization in competitive reinforcement learning
|
https://scholar.google.com/scholar?cluster=547818193126660523&hl=en&as_sdt=0,5
| 12 | 2,022 |
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets
| 1 |
neurips
| 3 | 0 |
2023-06-16 22:58:06.091000
|
https://github.com/google-research/tabnas
| 6 |
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets
|
https://scholar.google.com/scholar?cluster=8517070308098238947&hl=en&as_sdt=0,5
| 4 | 2,022 |
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:58:06.304000
|
https://github.com/roxie62/embed-and-emulate
| 5 |
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification
|
https://scholar.google.com/scholar?cluster=8584482491663793021&hl=en&as_sdt=0,43
| 1 | 2,022 |
Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech
| 3 |
neurips
| 9 | 1 |
2023-06-16 22:58:06.516000
|
https://github.com/zain-jiang/dict-tts
| 120 |
Dict-tts: Learning to pronounce with prior dictionary knowledge for text-to-speech
|
https://scholar.google.com/scholar?cluster=18386504940057315518&hl=en&as_sdt=0,5
| 7 | 2,022 |
Task-Agnostic Graph Explanations
| 9 |
neurips
| 239 | 19 |
2023-06-16 22:58:06.726000
|
https://github.com/divelab/DIG
| 1,503 |
Task-agnostic graph explanations
|
https://scholar.google.com/scholar?cluster=17298628046382170776&hl=en&as_sdt=0,5
| 33 | 2,022 |
Embrace the Gap: VAEs Perform Independent Mechanism Analysis
| 4 |
neurips
| 0 | 0 |
2023-06-16 22:58:06.938000
|
https://github.com/rpatrik96/ima-vae
| 19 |
Embrace the Gap: VAEs Perform Independent Mechanism Analysis
|
https://scholar.google.com/scholar?cluster=2566376193853302421&hl=en&as_sdt=0,14
| 3 | 2,022 |
Improved Feature Distillation via Projector Ensemble
| 4 |
neurips
| 3 | 0 |
2023-06-16 22:58:07.150000
|
https://github.com/chenyd7/pefd
| 18 |
Improved Feature Distillation via Projector Ensemble
|
https://scholar.google.com/scholar?cluster=7163318270535099201&hl=en&as_sdt=0,45
| 1 | 2,022 |
Introspective Learning : A Two-Stage approach for Inference in Neural Networks
| 6 |
neurips
| 1 | 0 |
2023-06-16 22:58:07.362000
|
https://github.com/olivesgatech/introspective-learning
| 4 |
Introspective learning: A two-stage approach for inference in neural networks
|
https://scholar.google.com/scholar?cluster=16860968089703315753&hl=en&as_sdt=0,5
| 5 | 2,022 |
Bayesian Active Learning with Fully Bayesian Gaussian Processes
| 5 |
neurips
| 1 | 0 |
2023-06-16 22:58:07.574000
|
https://github.com/CoRiis/active-learning-fbgp
| 3 |
Bayesian active learning with fully Bayesian Gaussian processes
|
https://scholar.google.com/scholar?cluster=7248161076733979181&hl=en&as_sdt=0,39
| 1 | 2,022 |
In Defense of the Unitary Scalarization for Deep Multi-Task Learning
| 25 |
neurips
| 1 | 0 |
2023-06-16 22:58:07.786000
|
https://github.com/yobibyte/unitary-scalarization-dmtl
| 12 |
In defense of the unitary scalarization for deep multi-task learning
|
https://scholar.google.com/scholar?cluster=11217111018249719675&hl=en&as_sdt=0,5
| 2 | 2,022 |
Tempo: Accelerating Transformer-Based Model Training through Memory Footprint Reduction
| 0 |
neurips
| 1 | 0 |
2023-06-16 22:58:07.998000
|
https://github.com/uoft-ecosystem/tempo
| 13 |
Tempo: Accelerating Transformer-Based Model Training through Memory Footprint Reduction
|
https://scholar.google.com/scholar?cluster=4376824659528049474&hl=en&as_sdt=0,14
| 1 | 2,022 |
AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning
| 0 |
neurips
| 1 | 0 |
2023-06-16 22:58:08.224000
|
https://github.com/taoyang225/ad-drop
| 17 |
AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning
|
https://scholar.google.com/scholar?cluster=15838069514720957159&hl=en&as_sdt=0,5
| 1 | 2,022 |
Reinforced Genetic Algorithm for Structure-based Drug Design
| 7 |
neurips
| 5 | 1 |
2023-06-16 22:58:08.438000
|
https://github.com/futianfan/reinforced-genetic-algorithm
| 43 |
Reinforced genetic algorithm for structure-based drug design
|
https://scholar.google.com/scholar?cluster=7318600000502726060&hl=en&as_sdt=0,5
| 1 | 2,022 |
A Variational Edge Partition Model for Supervised Graph Representation Learning
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:58:08.649000
|
https://github.com/yh-utmsb/vepm
| 3 |
A Variational Edge Partition Model for Supervised Graph Representation Learning
|
https://scholar.google.com/scholar?cluster=13490644048075543667&hl=en&as_sdt=0,5
| 2 | 2,022 |
Learning Optimal Flows for Non-Equilibrium Importance Sampling
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:58:08.861000
|
https://github.com/yucaoyc/neis
| 2 |
Learning optimal flows for non-equilibrium importance sampling
|
https://scholar.google.com/scholar?cluster=4036926059814968086&hl=en&as_sdt=0,5
| 1 | 2,022 |
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks
| 7 |
neurips
| 3 | 0 |
2023-06-16 22:58:09.073000
|
https://github.com/rtu715/nas-bench-360
| 41 |
NAS-bench-360: Benchmarking neural architecture search on diverse tasks
|
https://scholar.google.com/scholar?cluster=16735333097872491854&hl=en&as_sdt=0,5
| 4 | 2,022 |
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
| 6 |
neurips
| 1 | 0 |
2023-06-16 22:58:09.285000
|
https://github.com/tychovdo/lila
| 15 |
Invariance learning in deep neural networks with differentiable Laplace approximations
|
https://scholar.google.com/scholar?cluster=1429247926698502662&hl=en&as_sdt=0,14
| 1 | 2,022 |
QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query
| 1 |
neurips
| 6 | 0 |
2023-06-16 22:58:09.497000
|
https://github.com/buptxyb666/querypose
| 18 |
QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query
|
https://scholar.google.com/scholar?cluster=15275535778333612126&hl=en&as_sdt=0,10
| 6 | 2,022 |
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:58:09.714000
|
https://github.com/flowersteam/eager
| 8 |
Eager: Asking and answering questions for automatic reward shaping in language-guided rl
|
https://scholar.google.com/scholar?cluster=15918390192447267703&hl=en&as_sdt=0,5
| 2 | 2,022 |
OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters
| 3 |
neurips
| 1 | 0 |
2023-06-16 22:58:09.926000
|
https://github.com/ellisalicante/openfilter
| 4 |
OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters
|
https://scholar.google.com/scholar?cluster=5342281461625207848&hl=en&as_sdt=0,10
| 1 | 2,022 |
Improving Policy Learning via Language Dynamics Distillation
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:58:10.138000
|
https://github.com/vzhong/language-dynamics-distillation
| 7 |
Improving Policy Learning via Language Dynamics Distillation
|
https://scholar.google.com/scholar?cluster=6541543257718525054&hl=en&as_sdt=0,33
| 1 | 2,022 |
The Neural Testbed: Evaluating Joint Predictions
| 6 |
neurips
| 13 | 2 |
2023-06-16 22:58:10.350000
|
https://github.com/deepmind/neural_testbed
| 181 |
The neural testbed: Evaluating joint predictions
|
https://scholar.google.com/scholar?cluster=9820249592356438993&hl=en&as_sdt=0,34
| 12 | 2,022 |
Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization
| 0 |
neurips
| 0 | 1 |
2023-06-16 22:58:10.562000
|
https://github.com/rehg-lab/dope_selfsup
| 9 |
Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization
|
https://scholar.google.com/scholar?cluster=15982502156616388651&hl=en&as_sdt=0,37
| 2 | 2,022 |
Teacher Forcing Recovers Reward Functions for Text Generation
| 4 |
neurips
| 1 | 1 |
2023-06-16 22:58:10.773000
|
https://github.com/manga-uofa/lmreward
| 16 |
Teacher Forcing Recovers Reward Functions for Text Generation
|
https://scholar.google.com/scholar?cluster=8015164160931191027&hl=en&as_sdt=0,26
| 3 | 2,022 |
Masked Autoencoding for Scalable and Generalizable Decision Making
| 6 |
neurips
| 1 | 0 |
2023-06-16 22:58:10.986000
|
https://github.com/fangchenliu/maskdp_public
| 24 |
Masked Autoencoding for Scalable and Generalizable Decision Making
|
https://scholar.google.com/scholar?cluster=5876325032505210747&hl=en&as_sdt=0,33
| 1 | 2,022 |
Distributional Reward Estimation for Effective Multi-agent Deep Reinforcement Learning
| 0 |
neurips
| 1 | 1 |
2023-06-16 22:58:11.197000
|
https://github.com/jf-hu/dre-marl
| 3 |
Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement Learning
|
https://scholar.google.com/scholar?cluster=4608771757173948810&hl=en&as_sdt=0,44
| 1 | 2,022 |
ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler
| 2 |
neurips
| 5 | 1 |
2023-06-16 22:58:11.420000
|
https://github.com/neurasearch/neurips-2022-submission-3358
| 18 |
ELASTIC: numerical reasoning with adaptive symbolic compiler
|
https://scholar.google.com/scholar?cluster=6782406897046377184&hl=en&as_sdt=0,47
| 1 | 2,022 |
Training Spiking Neural Networks with Local Tandem Learning
| 6 |
neurips
| 0 | 0 |
2023-06-16 22:58:11.632000
|
https://github.com/aries231/local_tandem_learning_rule
| 4 |
Training Spiking Neural Networks with Local Tandem Learning
|
https://scholar.google.com/scholar?cluster=213134529644040528&hl=en&as_sdt=0,33
| 1 | 2,022 |
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
| 17 |
neurips
| 4 | 1 |
2023-06-16 22:58:11.844000
|
https://github.com/tianzhou2011/FiLM
| 59 |
Film: Frequency improved legendre memory model for long-term time series forecasting
|
https://scholar.google.com/scholar?cluster=13865604697725904904&hl=en&as_sdt=0,14
| 1 | 2,022 |
Scalable Neural Video Representations with Learnable Positional Features
| 10 |
neurips
| 3 | 0 |
2023-06-16 22:58:12.056000
|
https://github.com/subin-kim-cv/NVP
| 54 |
Scalable neural video representations with learnable positional features
|
https://scholar.google.com/scholar?cluster=418170278424044647&hl=en&as_sdt=0,5
| 5 | 2,022 |
Data Augmentation MCMC for Bayesian Inference from Privatized Data
| 7 |
neurips
| 0 | 0 |
2023-06-16 22:58:12.269000
|
https://github.com/nianqiaoju/dataaugmentation-mcmc-differentialprivacy
| 1 |
Data augmentation MCMC for bayesian inference from privatized data
|
https://scholar.google.com/scholar?cluster=15062825802466844692&hl=en&as_sdt=0,45
| 3 | 2,022 |
Verification and search algorithms for causal DAGs
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:58:12.481000
|
https://github.com/cxjdavin/verification-and-search-algorithms-for-causal-dags
| 1 |
Verification and search algorithms for causal DAGs
|
https://scholar.google.com/scholar?cluster=5973326212150461189&hl=en&as_sdt=0,49
| 1 | 2,022 |
Learning Equivariant Segmentation with Instance-Unique Querying
| 12 |
neurips
| 0 | 4 |
2023-06-16 22:58:12.693000
|
https://github.com/jamesliang819/instance_unique_querying
| 20 |
Learning Equivariant Segmentation with Instance-Unique Querying
|
https://scholar.google.com/scholar?cluster=258748143190805226&hl=en&as_sdt=0,5
| 2 | 2,022 |
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training
| 10 |
neurips
| 2 | 1 |
2023-06-16 22:58:12.904000
|
https://github.com/pdejorge/n-fgsm
| 18 |
Make some noise: Reliable and efficient single-step adversarial training
|
https://scholar.google.com/scholar?cluster=4411689672191629194&hl=en&as_sdt=0,31
| 1 | 2,022 |
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal
| 6 |
neurips
| 1 | 4 |
2023-06-16 22:58:13.116000
|
https://github.com/shiyuchengtju/par
| 6 |
Decision-based black-box attack against vision transformers via patch-wise adversarial removal
|
https://scholar.google.com/scholar?cluster=10811327262491458953&hl=en&as_sdt=0,5
| 1 | 2,022 |
Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:58:13.327000
|
https://github.com/Laborieux-Axel/holomorphic_eqprop
| 6 |
Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations
|
https://scholar.google.com/scholar?cluster=2660045405208851732&hl=en&as_sdt=0,47
| 1 | 2,022 |
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning
| 27 |
neurips
| 5 | 3 |
2023-06-16 22:58:13.545000
|
https://github.com/ylsung/ladder-side-tuning
| 150 |
Lst: Ladder side-tuning for parameter and memory efficient transfer learning
|
https://scholar.google.com/scholar?cluster=5847102735661395022&hl=en&as_sdt=0,5
| 2 | 2,022 |
Amortized Inference for Causal Structure Learning
| 5 |
neurips
| 4 | 0 |
2023-06-16 22:58:13.756000
|
https://github.com/larslorch/avici
| 30 |
Amortized inference for causal structure learning
|
https://scholar.google.com/scholar?cluster=12367761673759456964&hl=en&as_sdt=0,33
| 1 | 2,022 |
Selective compression learning of latent representations for variable-rate image compression
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:58:13.968000
|
https://github.com/jooyoungleeetri/scr
| 17 |
Selective compression learning of latent representations for variable-rate image compression
|
https://scholar.google.com/scholar?cluster=13909155481844367559&hl=en&as_sdt=0,11
| 1 | 2,022 |
Multi-LexSum: Real-world Summaries of Civil Rights Lawsuits at Multiple Granularities
| 13 |
neurips
| 0 | 0 |
2023-06-16 22:58:14.181000
|
https://github.com/multilexsum/dataset
| 15 |
Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities
|
https://scholar.google.com/scholar?cluster=5062559571179489712&hl=en&as_sdt=0,33
| 0 | 2,022 |
Local Bayesian optimization via maximizing probability of descent
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:58:14.425000
|
https://github.com/kayween/local-bo-mpd
| 6 |
Local Bayesian optimization via maximizing probability of descent
|
https://scholar.google.com/scholar?cluster=3485637555120117353&hl=en&as_sdt=0,44
| 3 | 2,022 |
Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection
| 14 |
neurips
| 2 | 1 |
2023-06-16 22:58:14.636000
|
https://github.com/thuyimingli/untargeted_backdoor_watermark
| 36 |
Untargeted backdoor watermark: Towards harmless and stealthy dataset copyright protection
|
https://scholar.google.com/scholar?cluster=741958679609205316&hl=en&as_sdt=0,18
| 4 | 2,022 |
Learning Symmetric Rules with SATNet
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:58:14.849000
|
https://github.com/Lim-Sangho/SymSATNet
| 0 |
Learning Symmetric Rules with SATNet
|
https://scholar.google.com/scholar?cluster=13706901058852825985&hl=en&as_sdt=0,5
| 2 | 2,022 |
Langevin Autoencoders for Learning Deep Latent Variable Models
| 0 |
neurips
| 1 | 0 |
2023-06-16 22:58:15.061000
|
https://github.com/ishohei220/lae
| 5 |
Langevin Autoencoders for Learning Deep Latent Variable Models
|
https://scholar.google.com/scholar?cluster=4451193403290607763&hl=en&as_sdt=0,5
| 1 | 2,022 |
Fault-Aware Neural Code Rankers
| 12 |
neurips
| 5 | 1 |
2023-06-16 22:58:15.272000
|
https://github.com/microsoft/coderanker
| 22 |
Fault-aware neural code rankers
|
https://scholar.google.com/scholar?cluster=11520887599770538288&hl=en&as_sdt=0,5
| 4 | 2,022 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.