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Learning Data Manipulation for Augmentation and Weighting
| 106 |
neurips
| 18 | 5 |
2023-06-15 23:43:29.204000
|
https://github.com/tanyuqian/learning-data-manipulation
| 107 |
Learning data manipulation for augmentation and weighting
|
https://scholar.google.com/scholar?cluster=8112277645678768477&hl=en&as_sdt=0,11
| 6 | 2,019 |
Levenshtein Transformer
| 307 |
neurips
| 5,869 | 1,030 |
2023-06-15 23:43:29.386000
|
https://github.com/pytorch/fairseq
| 26,461 |
Levenshtein transformer
|
https://scholar.google.com/scholar?cluster=6969695107747166842&hl=en&as_sdt=0,5
| 411 | 2,019 |
Learning Perceptual Inference by Contrasting
| 82 |
neurips
| 3 | 0 |
2023-06-15 23:43:29.568000
|
https://github.com/WellyZhang/CoPINet
| 26 |
Learning perceptual inference by contrasting
|
https://scholar.google.com/scholar?cluster=6429330194267685212&hl=en&as_sdt=0,39
| 3 | 2,019 |
Image Captioning: Transforming Objects into Words
| 375 |
neurips
| 46 | 13 |
2023-06-15 23:43:29.751000
|
https://github.com/yahoo/object_relation_transformer
| 167 |
Image captioning: Transforming objects into words
|
https://scholar.google.com/scholar?cluster=10363318255496251924&hl=en&as_sdt=0,15
| 8 | 2,019 |
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
| 707 |
neurips
| 209 | 30 |
2023-06-15 23:43:29.933000
|
https://github.com/descriptinc/melgan-neurips
| 844 |
Melgan: Generative adversarial networks for conditional waveform synthesis
|
https://scholar.google.com/scholar?cluster=3316540057684655113&hl=en&as_sdt=0,11
| 60 | 2,019 |
Deliberative Explanations: visualizing network insecurities
| 9 |
neurips
| 2 | 0 |
2023-06-15 23:43:30.115000
|
https://github.com/peiwang062/Deliberative-explanation
| 2 |
Deliberative explanations: visualizing network insecurities
|
https://scholar.google.com/scholar?cluster=7324304608131052861&hl=en&as_sdt=0,33
| 2 | 2,019 |
Uncoupled Regression from Pairwise Comparison Data
| 10 |
neurips
| 0 | 0 |
2023-06-15 23:43:30.298000
|
https://github.com/liyuan9988/UncoupledRegressionComparison
| 4 |
Uncoupled regression from pairwise comparison data
|
https://scholar.google.com/scholar?cluster=11084220127934527031&hl=en&as_sdt=0,5
| 1 | 2,019 |
Pareto Multi-Task Learning
| 198 |
neurips
| 27 | 3 |
2023-06-15 23:43:30.480000
|
https://github.com/Xi-L/ParetoMTL
| 94 |
Pareto multi-task learning
|
https://scholar.google.com/scholar?cluster=4838439418899055055&hl=en&as_sdt=0,5
| 1 | 2,019 |
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos
| 162 |
neurips
| 14 | 2 |
2023-06-15 23:43:30.663000
|
https://github.com/yytzsy/SCDM
| 67 |
Semantic conditioned dynamic modulation for temporal sentence grounding in videos
|
https://scholar.google.com/scholar?cluster=4012702168222045313&hl=en&as_sdt=0,14
| 3 | 2,019 |
A Domain Agnostic Measure for Monitoring and Evaluating GANs
| 37 |
neurips
| 0 | 1 |
2023-06-15 23:43:30.845000
|
https://github.com/pgrnar/DualityGap
| 5 |
A domain agnostic measure for monitoring and evaluating GANs
|
https://scholar.google.com/scholar?cluster=15032346685874617570&hl=en&as_sdt=0,47
| 6 | 2,019 |
Enabling hyperparameter optimization in sequential autoencoders for spiking neural data
| 30 |
neurips
| 3 | 1 |
2023-06-15 23:43:31.027000
|
https://github.com/snel-repo/lfads-cd
| 6 |
Enabling hyperparameter optimization in sequential autoencoders for spiking neural data
|
https://scholar.google.com/scholar?cluster=1905318586909285690&hl=en&as_sdt=0,5
| 3 | 2,019 |
Grid Saliency for Context Explanations of Semantic Segmentation
| 29 |
neurips
| 1 | 3 |
2023-06-15 23:43:31.210000
|
https://github.com/boschresearch/GridSaliency-ToyDatasetGen
| 10 |
Grid saliency for context explanations of semantic segmentation
|
https://scholar.google.com/scholar?cluster=17400150270584494273&hl=en&as_sdt=0,5
| 5 | 2,019 |
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products
| 62 |
neurips
| 17 | 3 |
2023-06-15 23:43:31.391000
|
https://github.com/Tharun24/MACH
| 45 |
Extreme classification in log memory using count-min sketch: A case study of amazon search with 50m products
|
https://scholar.google.com/scholar?cluster=2998064929794090427&hl=en&as_sdt=0,14
| 6 | 2,019 |
Selecting the independent coordinates of manifolds with large aspect ratios
| 11 |
neurips
| 0 | 0 |
2023-06-15 23:43:31.573000
|
https://github.com/yuchaz/independent_coordinate_search
| 1 |
Selecting the independent coordinates of manifolds with large aspect ratios
|
https://scholar.google.com/scholar?cluster=6960980108691938580&hl=en&as_sdt=0,46
| 3 | 2,019 |
DM2C: Deep Mixed-Modal Clustering
| 26 |
neurips
| 1 | 2 |
2023-06-15 23:43:31.756000
|
https://github.com/jiangyangby/DM2C
| 11 |
Dm2c: Deep mixed-modal clustering
|
https://scholar.google.com/scholar?cluster=4258988165212066839&hl=en&as_sdt=0,5
| 2 | 2,019 |
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates
| 19 |
neurips
| 1 | 0 |
2023-06-15 23:43:31.938000
|
https://github.com/adil-salim/SPLA
| 0 |
Stochastic proximal langevin algorithm: Potential splitting and nonasymptotic rates
|
https://scholar.google.com/scholar?cluster=8964049524700423512&hl=en&as_sdt=0,47
| 1 | 2,019 |
Fast AutoAugment
| 531 |
neurips
| 197 | 27 |
2023-06-15 23:43:32.121000
|
https://github.com/kakaobrain/fast-autoaugment
| 1,558 |
Fast autoaugment
|
https://scholar.google.com/scholar?cluster=1889800553508296252&hl=en&as_sdt=0,5
| 40 | 2,019 |
A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI
| 7 |
neurips
| 0 | 0 |
2023-06-15 23:43:32.303000
|
https://github.com/taotu/VBLDS_Connectivity_EEG_fMRI
| 8 |
A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI
|
https://scholar.google.com/scholar?cluster=11225170596996891049&hl=en&as_sdt=0,39
| 1 | 2,019 |
Efficient Forward Architecture Search
| 38 |
neurips
| 22 | 1 |
2023-06-15 23:43:32.486000
|
https://github.com/microsoft/petridishnn
| 110 |
Efficient forward architecture search
|
https://scholar.google.com/scholar?cluster=28350854017058625&hl=en&as_sdt=0,14
| 14 | 2,019 |
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network
| 84 |
neurips
| 12 | 1 |
2023-06-15 23:43:32.669000
|
https://github.com/demonzyj56/E3Outlier
| 38 |
Effective end-to-end unsupervised outlier detection via inlier priority of discriminative network
|
https://scholar.google.com/scholar?cluster=5342789458391186972&hl=en&as_sdt=0,47
| 4 | 2,019 |
Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games
| 54 |
neurips
| 1 | 0 |
2023-06-15 23:43:32.851000
|
https://github.com/lamflokas/cycles
| 0 |
Poincaré recurrence, cycles and spurious equilibria in gradient-descent-ascent for non-convex non-concave zero-sum games
|
https://scholar.google.com/scholar?cluster=14231094102989983281&hl=en&as_sdt=0,14
| 2 | 2,019 |
End-to-End Learning on 3D Protein Structure for Interface Prediction
| 80 |
neurips
| 13 | 5 |
2023-06-15 23:43:33.034000
|
https://github.com/drorlab/DIPS
| 60 |
End-to-end learning on 3d protein structure for interface prediction
|
https://scholar.google.com/scholar?cluster=11547606784412884634&hl=en&as_sdt=0,10
| 16 | 2,019 |
Scalable Global Optimization via Local Bayesian Optimization
| 254 |
neurips
| 33 | 4 |
2023-06-15 23:43:33.216000
|
https://github.com/uber-research/TuRBO
| 138 |
Scalable global optimization via local bayesian optimization
|
https://scholar.google.com/scholar?cluster=4068527578266186377&hl=en&as_sdt=0,23
| 7 | 2,019 |
Positional Normalization
| 76 |
neurips
| 16 | 1 |
2023-06-15 23:43:33.406000
|
https://github.com/Boyiliee/PONO
| 146 |
Positional normalization
|
https://scholar.google.com/scholar?cluster=10490893363553766514&hl=en&as_sdt=0,5
| 9 | 2,019 |
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
| 38 |
neurips
| 55 | 38 |
2023-06-15 23:43:33.589000
|
https://github.com/pyprob/pyprob
| 386 |
Efficient probabilistic inference in the quest for physics beyond the standard model
|
https://scholar.google.com/scholar?cluster=375356109416148493&hl=en&as_sdt=0,33
| 36 | 2,019 |
Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs
| 4 |
neurips
| 11 | 0 |
2023-06-15 23:43:33.771000
|
https://github.com/stanis-morozov/prodige
| 47 |
Beyond vector spaces: Compact data representation as differentiable weighted graphs
|
https://scholar.google.com/scholar?cluster=3714868262045801223&hl=en&as_sdt=0,5
| 5 | 2,019 |
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation
| 232 |
neurips
| 20 | 10 |
2023-06-15 23:43:33.954000
|
https://github.com/RogerZhangzz/CAG_UDA
| 135 |
Category anchor-guided unsupervised domain adaptation for semantic segmentation
|
https://scholar.google.com/scholar?cluster=5741374386417443357&hl=en&as_sdt=0,39
| 5 | 2,019 |
Novel positional encodings to enable tree-based transformers
| 108 |
neurips
| 49 | 10 |
2023-06-15 23:43:34.136000
|
https://github.com/microsoft/icecaps
| 283 |
Novel positional encodings to enable tree-based transformers
|
https://scholar.google.com/scholar?cluster=8745417942122294740&hl=en&as_sdt=0,5
| 31 | 2,019 |
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
| 132 |
neurips
| 10 | 1 |
2023-06-15 23:43:34.319000
|
https://github.com/HongtengXu/s-gwl
| 31 |
Scalable gromov-wasserstein learning for graph partitioning and matching
|
https://scholar.google.com/scholar?cluster=17818306347293669263&hl=en&as_sdt=0,5
| 2 | 2,019 |
Deep Set Prediction Networks
| 92 |
neurips
| 17 | 2 |
2023-06-15 23:43:34.502000
|
https://github.com/Cyanogenoid/dspn
| 97 |
Deep set prediction networks
|
https://scholar.google.com/scholar?cluster=1113560646792223618&hl=en&as_sdt=0,33
| 5 | 2,019 |
A unified theory for the origin of grid cells through the lens of pattern formation
| 61 |
neurips
| 14 | 2 |
2023-06-15 23:43:34.684000
|
https://github.com/ganguli-lab/grid-pattern-formation
| 38 |
A unified theory for the origin of grid cells through the lens of pattern formation
|
https://scholar.google.com/scholar?cluster=14776833330125536661&hl=en&as_sdt=0,11
| 19 | 2,019 |
Functional Adversarial Attacks
| 153 |
neurips
| 6 | 1 |
2023-06-15 23:43:34.867000
|
https://github.com/cassidylaidlaw/ReColorAdv
| 31 |
Functional adversarial attacks
|
https://scholar.google.com/scholar?cluster=1676214359814686616&hl=en&as_sdt=0,7
| 2 | 2,019 |
Memory-oriented Decoder for Light Field Salient Object Detection
| 80 |
neurips
| 1 | 0 |
2023-06-15 23:43:35.049000
|
https://github.com/OIPLab-DUT/MoLF
| 5 |
Memory-oriented decoder for light field salient object detection
|
https://scholar.google.com/scholar?cluster=6967318587141659814&hl=en&as_sdt=0,5
| 1 | 2,019 |
Learning from Trajectories via Subgoal Discovery
| 33 |
neurips
| 2 | 1 |
2023-06-15 23:43:35.231000
|
https://github.com/sujoyp/subgoal-discovery
| 12 |
Learning from trajectories via subgoal discovery
|
https://scholar.google.com/scholar?cluster=16236425199036856550&hl=en&as_sdt=0,36
| 2 | 2,019 |
Unsupervised State Representation Learning in Atari
| 219 |
neurips
| 50 | 10 |
2023-06-15 23:43:35.414000
|
https://github.com/mila-iqia/atari-representation-learning
| 226 |
Unsupervised state representation learning in atari
|
https://scholar.google.com/scholar?cluster=6441557733735697646&hl=en&as_sdt=0,39
| 16 | 2,019 |
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning
| 9 |
neurips
| 0 | 0 |
2023-06-15 23:43:35.596000
|
https://github.com/oxwhirl/loaded-dice
| 8 |
Loaded DiCE: Trading off bias and variance in any-order score function gradient estimators for reinforcement learning
|
https://scholar.google.com/scholar?cluster=12610147229310871912&hl=en&as_sdt=0,10
| 5 | 2,019 |
Meta Learning with Relational Information for Short Sequences
| 15 |
neurips
| 1 | 0 |
2023-06-15 23:43:35.779000
|
https://github.com/HMJiangGatech/harmless
| 4 |
Meta learning with relational information for short sequences
|
https://scholar.google.com/scholar?cluster=15009113702516018640&hl=en&as_sdt=0,44
| 3 | 2,019 |
Kernel quadrature with DPPs
| 36 |
neurips
| 0 | 0 |
2023-06-15 23:43:35.961000
|
https://github.com/AyoubBelhadji/DPPKQ
| 0 |
Kernel quadrature with DPPs
|
https://scholar.google.com/scholar?cluster=93716723923556238&hl=en&as_sdt=0,5
| 2 | 2,019 |
A Debiased MDI Feature Importance Measure for Random Forests
| 70 |
neurips
| 0 | 1 |
2023-06-15 23:43:36.144000
|
https://github.com/shifwang/paper-debiased-feature-importance
| 3 |
A debiased MDI feature importance measure for random forests
|
https://scholar.google.com/scholar?cluster=6510754319433333481&hl=en&as_sdt=0,5
| 2 | 2,019 |
MintNet: Building Invertible Neural Networks with Masked Convolutions
| 57 |
neurips
| 7 | 4 |
2023-06-15 23:43:36.326000
|
https://github.com/ermongroup/mintnet
| 37 |
Mintnet: Building invertible neural networks with masked convolutions
|
https://scholar.google.com/scholar?cluster=14647518229327139613&hl=en&as_sdt=0,33
| 6 | 2,019 |
Learning Temporal Pose Estimation from Sparsely-Labeled Videos
| 57 |
neurips
| 15 | 8 |
2023-06-15 23:43:36.508000
|
https://github.com/facebookresearch/PoseWarper
| 121 |
Learning temporal pose estimation from sparsely-labeled videos
|
https://scholar.google.com/scholar?cluster=1801466269510518613&hl=en&as_sdt=0,5
| 8 | 2,019 |
On the equivalence between graph isomorphism testing and function approximation with GNNs
| 209 |
neurips
| 5 | 1 |
2023-06-15 23:43:36.691000
|
https://github.com/leichen2018/Ring-GNN
| 12 |
On the equivalence between graph isomorphism testing and function approximation with gnns
|
https://scholar.google.com/scholar?cluster=12691711476883209&hl=en&as_sdt=0,14
| 3 | 2,019 |
Information Competing Process for Learning Diversified Representations
| 14 |
neurips
| 0 | 1 |
2023-06-15 23:43:36.873000
|
https://github.com/hujiecpp/InformationCompetingProcess
| 17 |
Information competing process for learning diversified representations
|
https://scholar.google.com/scholar?cluster=4705195957612955232&hl=en&as_sdt=0,33
| 3 | 2,019 |
On Relating Explanations and Adversarial Examples
| 104 |
neurips
| 0 | 0 |
2023-06-15 23:43:37.056000
|
https://github.com/alexeyignatiev/xpce-duality
| 3 |
On relating explanations and adversarial examples
|
https://scholar.google.com/scholar?cluster=13118428482617248562&hl=en&as_sdt=0,5
| 2 | 2,019 |
Greedy Sampling for Approximate Clustering in the Presence of Outliers
| 18 |
neurips
| 1 | 0 |
2023-06-15 23:43:37.238000
|
https://github.com/Sharvaree/KMeans_Experiments
| 1 |
Greedy sampling for approximate clustering in the presence of outliers
|
https://scholar.google.com/scholar?cluster=18078709320029715659&hl=en&as_sdt=0,10
| 3 | 2,019 |
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
| 116 |
neurips
| 4 | 1 |
2023-06-15 23:43:37.421000
|
https://github.com/nimadehmamy/Understanding-GCN
| 38 |
Understanding the representation power of graph neural networks in learning graph topology
|
https://scholar.google.com/scholar?cluster=4481929579927594598&hl=en&as_sdt=0,5
| 5 | 2,019 |
Single-Model Uncertainties for Deep Learning
| 198 |
neurips
| 15 | 0 |
2023-06-15 23:43:37.604000
|
https://github.com/facebookresearch/SingleModelUncertainty
| 60 |
Single-model uncertainties for deep learning
|
https://scholar.google.com/scholar?cluster=12778462309465279243&hl=en&as_sdt=0,5
| 5 | 2,019 |
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric
| 61 |
neurips
| 0 | 0 |
2023-06-15 23:43:37.787000
|
https://github.com/CausalML/xauc
| 4 |
The fairness of risk scores beyond classification: Bipartite ranking and the xauc metric
|
https://scholar.google.com/scholar?cluster=12656617424346800106&hl=en&as_sdt=0,18
| 3 | 2,019 |
Wasserstein Weisfeiler-Lehman Graph Kernels
| 164 |
neurips
| 15 | 3 |
2023-06-15 23:43:37.970000
|
https://github.com/BorgwardtLab/WWL
| 67 |
Wasserstein weisfeiler-lehman graph kernels
|
https://scholar.google.com/scholar?cluster=6976031050358812991&hl=en&as_sdt=0,5
| 6 | 2,019 |
DATA: Differentiable ArchiTecture Approximation
| 45 |
neurips
| 0 | 1 |
2023-06-15 23:43:38.153000
|
https://github.com/XinbangZhang/DATA-NAS
| 11 |
Data: Differentiable architecture approximation
|
https://scholar.google.com/scholar?cluster=17466991062887960112&hl=en&as_sdt=0,39
| 4 | 2,019 |
Fast Efficient Hyperparameter Tuning for Policy Gradient Methods
| 33 |
neurips
| 3 | 1 |
2023-06-15 23:43:38.335000
|
https://github.com/supratikp/HOOF
| 17 |
Fast efficient hyperparameter tuning for policy gradient methods
|
https://scholar.google.com/scholar?cluster=18256524196894232759&hl=en&as_sdt=0,5
| 3 | 2,019 |
Fast Structured Decoding for Sequence Models
| 96 |
neurips
| 0 | 0 |
2023-06-15 23:43:38.517000
|
https://github.com/Edward-Sun/structured-nart
| 14 |
Fast structured decoding for sequence models
|
https://scholar.google.com/scholar?cluster=2109712873142708905&hl=en&as_sdt=0,5
| 6 | 2,019 |
Guided Similarity Separation for Image Retrieval
| 39 |
neurips
| 7 | 4 |
2023-06-15 23:43:38.700000
|
https://github.com/layer6ai-labs/GSS
| 65 |
Guided similarity separation for image retrieval
|
https://scholar.google.com/scholar?cluster=12527388362392990303&hl=en&as_sdt=0,3
| 7 | 2,019 |
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks
| 156 |
neurips
| 18 | 0 |
2023-06-15 23:43:38.882000
|
https://github.com/kamwoh/DeepIPR
| 63 |
Rethinking deep neural network ownership verification: Embedding passports to defeat ambiguity attacks
|
https://scholar.google.com/scholar?cluster=5775759195048878084&hl=en&as_sdt=0,5
| 2 | 2,019 |
Addressing Failure Prediction by Learning Model Confidence
| 198 |
neurips
| 30 | 0 |
2023-06-15 23:43:39.065000
|
https://github.com/valeoai/ConfidNet
| 149 |
Addressing failure prediction by learning model confidence
|
https://scholar.google.com/scholar?cluster=2867131902793640249&hl=en&as_sdt=0,33
| 7 | 2,019 |
Communication-efficient Distributed SGD with Sketching
| 146 |
neurips
| 8 | 1 |
2023-06-15 23:43:39.248000
|
https://github.com/dhroth/sketchedsgd
| 26 |
Communication-efficient distributed SGD with sketching
|
https://scholar.google.com/scholar?cluster=16388029036104596741&hl=en&as_sdt=0,5
| 4 | 2,019 |
Exponential Family Estimation via Adversarial Dynamics Embedding
| 44 |
neurips
| 3 | 0 |
2023-06-15 23:43:39.439000
|
https://github.com/lzzcd001/ade-code
| 13 |
Exponential family estimation via adversarial dynamics embedding
|
https://scholar.google.com/scholar?cluster=9361110386553111889&hl=en&as_sdt=0,5
| 4 | 2,019 |
Towards Automatic Concept-based Explanations
| 400 |
neurips
| 37 | 8 |
2023-06-15 23:43:39.622000
|
https://github.com/amiratag/ACE
| 140 |
Towards automatic concept-based explanations
|
https://scholar.google.com/scholar?cluster=16711649168989026855&hl=en&as_sdt=0,33
| 8 | 2,019 |
Defending Neural Backdoors via Generative Distribution Modeling
| 118 |
neurips
| 3 | 0 |
2023-06-15 23:43:39.804000
|
https://github.com/superrrpotato/Defending-Neural-Backdoors-via-Generative-Distribution-Modeling
| 30 |
Defending neural backdoors via generative distribution modeling
|
https://scholar.google.com/scholar?cluster=9257022899586805044&hl=en&as_sdt=0,33
| 4 | 2,019 |
Offline Contextual Bayesian Optimization
| 25 |
neurips
| 2 | 1 |
2023-06-15 23:43:39.986000
|
https://github.com/fusion-ml/OCBO
| 8 |
Offline contextual bayesian optimization
|
https://scholar.google.com/scholar?cluster=14250666700551486212&hl=en&as_sdt=0,5
| 6 | 2,019 |
Uncertainty on Asynchronous Time Event Prediction
| 26 |
neurips
| 4 | 0 |
2023-06-15 23:43:40.169000
|
https://github.com/sharpenb/Uncertainty-Event-Prediction
| 18 |
Uncertainty on asynchronous time event prediction
|
https://scholar.google.com/scholar?cluster=1453508021322991763&hl=en&as_sdt=0,14
| 1 | 2,019 |
Hierarchical Decision Making by Generating and Following Natural Language Instructions
| 51 |
neurips
| 31 | 2 |
2023-06-15 23:43:40.354000
|
https://github.com/facebookresearch/minirts
| 154 |
Hierarchical decision making by generating and following natural language instructions
|
https://scholar.google.com/scholar?cluster=12924202693815963&hl=en&as_sdt=0,33
| 11 | 2,019 |
Structured Prediction with Projection Oracles
| 19 |
neurips
| 2 | 0 |
2023-06-15 23:43:40.537000
|
https://github.com/mblondel/projection-losses
| 25 |
Structured prediction with projection oracles
|
https://scholar.google.com/scholar?cluster=16227835173432942621&hl=en&as_sdt=0,5
| 3 | 2,019 |
Sobolev Independence Criterion
| 3 |
neurips
| 11 | 1 |
2023-06-15 23:43:40.719000
|
https://github.com/IBM/SIC
| 12 |
Sobolev independence criterion
|
https://scholar.google.com/scholar?cluster=10351062325018710141&hl=en&as_sdt=0,33
| 11 | 2,019 |
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions
| 42 |
neurips
| 0 | 0 |
2023-06-15 23:43:40.903000
|
https://github.com/aswilson07/ARGD
| 2 |
Accelerating rescaled gradient descent: Fast optimization of smooth functions
|
https://scholar.google.com/scholar?cluster=3984857145166519117&hl=en&as_sdt=0,5
| 2 | 2,019 |
Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases
| 6 |
neurips
| 0 | 0 |
2023-06-15 23:43:41.085000
|
https://github.com/xiyangl3/adp-estimator
| 8 |
Minimax optimal estimation of approximate differential privacy on neighboring databases
|
https://scholar.google.com/scholar?cluster=11105669156455896509&hl=en&as_sdt=0,3
| 2 | 2,019 |
Neural Spline Flows
| 450 |
neurips
| 42 | 5 |
2023-06-15 23:43:41.269000
|
https://github.com/bayesiains/nsf
| 222 |
Neural spline flows
|
https://scholar.google.com/scholar?cluster=8875670325745695973&hl=en&as_sdt=0,44
| 12 | 2,019 |
Embedding Symbolic Knowledge into Deep Networks
| 76 |
neurips
| 10 | 3 |
2023-06-15 23:43:41.451000
|
https://github.com/ZiweiXU/LENSR
| 32 |
Embedding symbolic knowledge into deep networks
|
https://scholar.google.com/scholar?cluster=14720048438970687985&hl=en&as_sdt=0,32
| 4 | 2,019 |
Partitioning Structure Learning for Segmented Linear Regression Trees
| 2 |
neurips
| 1 | 0 |
2023-06-15 23:43:41.635000
|
https://github.com/xy-zheng/Segmented-Linear-Regression-Tree
| 7 |
Partitioning structure learning for segmented linear regression trees
|
https://scholar.google.com/scholar?cluster=4768423146676252730&hl=en&as_sdt=0,5
| 3 | 2,019 |
Sparse Variational Inference: Bayesian Coresets from Scratch
| 34 |
neurips
| 30 | 1 |
2023-06-15 23:43:41.817000
|
https://github.com/trevorcampbell/bayesian-coresets
| 124 |
Sparse variational inference: Bayesian coresets from scratch
|
https://scholar.google.com/scholar?cluster=5409952380755212195&hl=en&as_sdt=0,5
| 8 | 2,019 |
Policy Evaluation with Latent Confounders via Optimal Balance
| 17 |
neurips
| 0 | 1 |
2023-06-15 23:43:41.999000
|
https://github.com/CausalML/LatentConfounderBalancing
| 3 |
Policy evaluation with latent confounders via optimal balance
|
https://scholar.google.com/scholar?cluster=18178264878955055838&hl=en&as_sdt=0,31
| 2 | 2,019 |
Dancing to Music
| 164 |
neurips
| 80 | 16 |
2023-06-15 23:43:42.182000
|
https://github.com/NVlabs/Dance2Music
| 505 |
Dancing to music
|
https://scholar.google.com/scholar?cluster=16920371227688956404&hl=en&as_sdt=0,5
| 45 | 2,019 |
Direct Estimation of Differential Functional Graphical Models
| 10 |
neurips
| 0 | 0 |
2023-06-15 23:43:42.369000
|
https://github.com/boxinz17/FuDGE
| 1 |
Direct estimation of differential functional graphical models
|
https://scholar.google.com/scholar?cluster=6229188529111598684&hl=en&as_sdt=0,33
| 3 | 2,019 |
Backpropagation-Friendly Eigendecomposition
| 43 |
neurips
| 11 | 3 |
2023-06-15 23:43:42.555000
|
https://github.com/WeiWangTrento/Power-Iteration-SVD
| 69 |
Backpropagation-friendly eigendecomposition
|
https://scholar.google.com/scholar?cluster=6440185494888261188&hl=en&as_sdt=0,34
| 4 | 2,019 |
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
| 114 |
neurips
| 17 | 2 |
2023-06-15 23:43:42.738000
|
https://github.com/KaosEngineer/PriorNetworks
| 51 |
Reverse kl-divergence training of prior networks: Improved uncertainty and adversarial robustness
|
https://scholar.google.com/scholar?cluster=11591831502126572935&hl=en&as_sdt=0,5
| 4 | 2,019 |
Adversarial Fisher Vectors for Unsupervised Representation Learning
| 10 |
neurips
| 19 | 1 |
2023-06-15 23:43:42.920000
|
https://github.com/apple/ml-afv
| 44 |
Adversarial fisher vectors for unsupervised representation learning
|
https://scholar.google.com/scholar?cluster=6777850722350187062&hl=en&as_sdt=0,5
| 17 | 2,019 |
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
| 44 |
neurips
| 3 | 1 |
2023-06-15 23:43:43.102000
|
https://github.com/gletarte/dichotomize-and-generalize
| 5 |
Dichotomize and generalize: PAC-Bayesian binary activated deep neural networks
|
https://scholar.google.com/scholar?cluster=12097211268555349606&hl=en&as_sdt=0,47
| 6 | 2,019 |
Approximate Feature Collisions in Neural Nets
| 5 |
neurips
| 0 | 0 |
2023-06-15 23:43:43.284000
|
https://github.com/zth667/Approximate-Feature-Collisions-in-Neural-Nets
| 2 |
Approximate feature collisions in neural nets
|
https://scholar.google.com/scholar?cluster=15639259790406372634&hl=en&as_sdt=0,33
| 2 | 2,019 |
Characterizing Bias in Classifiers using Generative Models
| 36 |
neurips
| 0 | 0 |
2023-06-15 23:43:43.467000
|
https://github.com/danmcduff/characterizingBias
| 1 |
Characterizing bias in classifiers using generative models
|
https://scholar.google.com/scholar?cluster=9354789485596756896&hl=en&as_sdt=0,5
| 1 | 2,019 |
Coresets for Archetypal Analysis
| 15 |
neurips
| 0 | 0 |
2023-06-15 23:43:43.649000
|
https://github.com/smair/archetypalanalysis-coreset
| 4 |
Coresets for archetypal analysis
|
https://scholar.google.com/scholar?cluster=7109457079600306157&hl=en&as_sdt=0,5
| 2 | 2,019 |
Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection
| 68 |
neurips
| 0 | 1 |
2023-06-15 23:43:43.832000
|
https://github.com/xgu1/DTM
| 2 |
Statistical analysis of nearest neighbor methods for anomaly detection
|
https://scholar.google.com/scholar?cluster=18002734610348809299&hl=en&as_sdt=0,7
| 1 | 2,019 |
Full-Gradient Representation for Neural Network Visualization
| 174 |
neurips
| 28 | 4 |
2023-06-15 23:43:44.014000
|
https://github.com/idiap/fullgrad-saliency
| 182 |
Full-gradient representation for neural network visualization
|
https://scholar.google.com/scholar?cluster=14256731466962538010&hl=en&as_sdt=0,5
| 7 | 2,019 |
Learnable Tree Filter for Structure-preserving Feature Transform
| 33 |
neurips
| 13 | 7 |
2023-06-15 23:43:44.196000
|
https://github.com/StevenGrove/TreeFilter-Torch
| 138 |
Learnable tree filter for structure-preserving feature transform
|
https://scholar.google.com/scholar?cluster=7316153313719053190&hl=en&as_sdt=0,5
| 10 | 2,019 |
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback
| 96 |
neurips
| 2 | 0 |
2023-06-15 23:43:44.378000
|
https://github.com/ZiyueHuang/dist-ef-sgdm
| 2 |
Communication-efficient distributed blockwise momentum SGD with error-feedback
|
https://scholar.google.com/scholar?cluster=15177903812893243410&hl=en&as_sdt=0,19
| 3 | 2,019 |
Coresets for Clustering with Fairness Constraints
| 88 |
neurips
| 0 | 9 |
2023-06-15 23:43:44.564000
|
https://github.com/sfjiang1990/Coresets-for-Clustering-with-Fairness-Constraints
| 1 |
Coresets for clustering with fairness constraints
|
https://scholar.google.com/scholar?cluster=13757547833601117696&hl=en&as_sdt=0,5
| 1 | 2,019 |
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
| 364 |
neurips
| 30 | 1 |
2023-06-15 23:43:44.757000
|
https://github.com/a1600012888/YOPO-You-Only-Propagate-Once
| 173 |
You only propagate once: Accelerating adversarial training via maximal principle
|
https://scholar.google.com/scholar?cluster=8806301774024240187&hl=en&as_sdt=0,5
| 8 | 2,019 |
Chasing Ghosts: Instruction Following as Bayesian State Tracking
| 59 |
neurips
| 4 | 2 |
2023-06-15 23:43:44.939000
|
https://github.com/batra-mlp-lab/vln-chasing-ghosts
| 9 |
Chasing ghosts: Instruction following as bayesian state tracking
|
https://scholar.google.com/scholar?cluster=11914100459452617998&hl=en&as_sdt=0,5
| 4 | 2,019 |
Rethinking the CSC Model for Natural Images
| 67 |
neurips
| 14 | 2 |
2023-06-15 23:43:45.121000
|
https://github.com/drorsimon/CSCNet
| 28 |
Rethinking the CSC model for natural images
|
https://scholar.google.com/scholar?cluster=8975540082038473364&hl=en&as_sdt=0,36
| 3 | 2,019 |
Max-value Entropy Search for Multi-Objective Bayesian Optimization
| 95 |
neurips
| 3 | 0 |
2023-06-15 23:43:45.303000
|
https://github.com/belakaria/MESMO
| 16 |
Max-value entropy search for multi-objective bayesian optimization
|
https://scholar.google.com/scholar?cluster=12951400276169505128&hl=en&as_sdt=0,5
| 2 | 2,019 |
Categorized Bandits
| 13 |
neurips
| 1 | 0 |
2023-06-15 23:43:45.486000
|
https://github.com/mjedor/categorized-bandits
| 2 |
Categorized bandits
|
https://scholar.google.com/scholar?cluster=1278360218254462409&hl=en&as_sdt=0,5
| 1 | 2,019 |
Curriculum-guided Hindsight Experience Replay
| 113 |
neurips
| 10 | 2 |
2023-06-15 23:43:45.669000
|
https://github.com/mengf1/CHER
| 51 |
Curriculum-guided hindsight experience replay
|
https://scholar.google.com/scholar?cluster=13835477089044998151&hl=en&as_sdt=0,5
| 4 | 2,019 |
Random Path Selection for Continual Learning
| 166 |
neurips
| 12 | 4 |
2023-06-15 23:43:45.852000
|
https://github.com/brjathu/RPSnet
| 50 |
Random path selection for continual learning
|
https://scholar.google.com/scholar?cluster=13661319739032626866&hl=en&as_sdt=0,14
| 2 | 2,019 |
On Single Source Robustness in Deep Fusion Models
| 23 |
neurips
| 7 | 2 |
2023-06-15 23:43:46.034000
|
https://github.com/twankim/avod_ssn
| 11 |
On single source robustness in deep fusion models
|
https://scholar.google.com/scholar?cluster=9475508091147138361&hl=en&as_sdt=0,5
| 3 | 2,019 |
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
| 239 |
neurips
| 16 | 7 |
2023-06-15 23:43:46.216000
|
https://github.com/steverab/failing-loudly
| 90 |
Failing loudly: An empirical study of methods for detecting dataset shift
|
https://scholar.google.com/scholar?cluster=17114748058005960595&hl=en&as_sdt=0,5
| 3 | 2,019 |
Shadowing Properties of Optimization Algorithms
| 14 |
neurips
| 0 | 0 |
2023-06-15 23:43:46.399000
|
https://github.com/aorvieto/shadowing
| 0 |
Shadowing properties of optimization algorithms
|
https://scholar.google.com/scholar?cluster=16930734437470236077&hl=en&as_sdt=0,5
| 1 | 2,019 |
Bayesian Batch Active Learning as Sparse Subset Approximation
| 104 |
neurips
| 13 | 1 |
2023-06-15 23:43:46.582000
|
https://github.com/rpinsler/active-bayesian-coresets
| 35 |
Bayesian batch active learning as sparse subset approximation
|
https://scholar.google.com/scholar?cluster=9791556257184579641&hl=en&as_sdt=0,33
| 3 | 2,019 |
Putting An End to End-to-End: Gradient-Isolated Learning of Representations
| 99 |
neurips
| 35 | 0 |
2023-06-15 23:43:46.764000
|
https://github.com/loeweX/Greedy_InfoMax
| 275 |
Putting an end to end-to-end: Gradient-isolated learning of representations
|
https://scholar.google.com/scholar?cluster=3627926315320048762&hl=en&as_sdt=0,5
| 17 | 2,019 |
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains
| 26 |
neurips
| 0 | 0 |
2023-06-15 23:43:46.946000
|
https://github.com/leaf-ai/muir
| 0 |
Modular universal reparameterization: Deep multi-task learning across diverse domains
|
https://scholar.google.com/scholar?cluster=5453919109038030817&hl=en&as_sdt=0,44
| 4 | 2,019 |
Decentralized Cooperative Stochastic Bandits
| 76 |
neurips
| 1 | 0 |
2023-06-15 23:43:47.129000
|
https://github.com/damaru2/decentralized-bandits
| 4 |
Decentralized cooperative stochastic bandits
|
https://scholar.google.com/scholar?cluster=1662602703149301964&hl=en&as_sdt=0,33
| 1 | 2,019 |
Powerset Convolutional Neural Networks
| 16 |
neurips
| 2 | 0 |
2023-06-15 23:43:47.312000
|
https://github.com/chrislybaer/Powerset-CNN
| 10 |
Powerset convolutional neural networks
|
https://scholar.google.com/scholar?cluster=8655459443031428222&hl=en&as_sdt=0,36
| 1 | 2,019 |
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