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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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