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Approximate Knowledge Compilation by Online Collapsed Importance Sampling | 21 | neurips | 0 | 0 | 2023-06-15 17:54:52.719000 | https://github.com/UCLA-StarAI/Collapsed-Compilation | 5 | Approximate knowledge compilation by online collapsed importance sampling | https://scholar.google.com/scholar?cluster=5801857808795259088&hl=en&as_sdt=0,10 | 4 | 2,018 |
Reversible Recurrent Neural Networks | 48 | neurips | 7 | 1 | 2023-06-15 17:54:52.912000 | https://github.com/matthewjmackay/reversible-rnn | 35 | Reversible recurrent neural networks | https://scholar.google.com/scholar?cluster=2936325833713118727&hl=en&as_sdt=0,44 | 3 | 2,018 |
Regularization Learning Networks: Deep Learning for Tabular Datasets | 79 | neurips | 6 | 2 | 2023-06-15 17:54:53.106000 | https://github.com/irashavitt/regularization_learning_networks | 31 | Regularization learning networks: deep learning for tabular datasets | https://scholar.google.com/scholar?cluster=12900371387873290272&hl=en&as_sdt=0,47 | 3 | 2,018 |
On Learning Intrinsic Rewards for Policy Gradient Methods | 160 | neurips | 10 | 2 | 2023-06-15 17:54:53.299000 | https://github.com/Hwhitetooth/lirpg | 54 | On learning intrinsic rewards for policy gradient methods | https://scholar.google.com/scholar?cluster=8658005357410230302&hl=en&as_sdt=0,45 | 4 | 2,018 |
Single-Agent Policy Tree Search With Guarantees | 27 | neurips | 15 | 1 | 2023-06-15 17:54:53.496000 | https://github.com/deepmind/boxoban-levels | 54 | Single-agent policy tree search with guarantees | https://scholar.google.com/scholar?cluster=17454634556201960088&hl=en&as_sdt=0,26 | 9 | 2,018 |
Bias and Generalization in Deep Generative Models: An Empirical Study | 99 | neurips | 8 | 0 | 2023-06-15 17:54:53.690000 | https://github.com/ermongroup/BiasAndGeneralization | 25 | Bias and generalization in deep generative models: An empirical study | https://scholar.google.com/scholar?cluster=17301681294706446940&hl=en&as_sdt=0,11 | 5 | 2,018 |
Link Prediction Based on Graph Neural Networks | 1,395 | neurips | 129 | 24 | 2023-06-15 17:54:53.883000 | https://github.com/muhanzhang/SEAL | 493 | Link prediction based on graph neural networks | https://scholar.google.com/scholar?cluster=11968553220977234326&hl=en&as_sdt=0,5 | 12 | 2,018 |
A flexible model for training action localization with varying levels of supervision | 41 | neurips | 6 | 1 | 2023-06-15 17:54:54.074000 | https://github.com/jalayrac/weakactionloc | 17 | A flexible model for training action localization with varying levels of supervision | https://scholar.google.com/scholar?cluster=12745987706790622376&hl=en&as_sdt=0,5 | 4 | 2,018 |
Generative Probabilistic Novelty Detection with Adversarial Autoencoders | 295 | neurips | 31 | 7 | 2023-06-15 17:54:54.264000 | https://github.com/podgorskiy/GPND | 125 | Generative probabilistic novelty detection with adversarial autoencoders | https://scholar.google.com/scholar?cluster=13335383760622553502&hl=en&as_sdt=0,3 | 11 | 2,018 |
Informative Features for Model Comparison | 24 | neurips | 3 | 0 | 2023-06-15 17:54:54.460000 | https://github.com/wittawatj/kernel-mod | 17 | Informative features for model comparison | https://scholar.google.com/scholar?cluster=962836959160034441&hl=en&as_sdt=0,10 | 6 | 2,018 |
Discrimination-aware Channel Pruning for Deep Neural Networks | 615 | neurips | 27 | 9 | 2023-06-15 17:54:54.650000 | https://github.com/SCUT-AILab/DCP | 179 | Discrimination-aware channel pruning for deep neural networks | https://scholar.google.com/scholar?cluster=4423411645597495&hl=en&as_sdt=0,10 | 9 | 2,018 |
On Fast Leverage Score Sampling and Optimal Learning | 78 | neurips | 2 | 1 | 2023-06-15 17:54:54.841000 | https://github.com/LCSL/bless | 12 | On fast leverage score sampling and optimal learning | https://scholar.google.com/scholar?cluster=6173645811972804817&hl=en&as_sdt=0,44 | 9 | 2,018 |
Robustness of conditional GANs to noisy labels | 174 | neurips | 9 | 2 | 2023-06-15 17:54:55.031000 | https://github.com/POLane16/Robust-Conditional-GAN | 39 | Robustness of conditional gans to noisy labels | https://scholar.google.com/scholar?cluster=4597323022745403664&hl=en&as_sdt=0,10 | 3 | 2,018 |
Legendre Decomposition for Tensors | 14 | neurips | 4 | 0 | 2023-06-15 17:54:55.222000 | https://github.com/mahito-sugiyama/Legendre-decomposition | 12 | Legendre decomposition for tensors | https://scholar.google.com/scholar?cluster=12973396671492815941&hl=en&as_sdt=0,10 | 2 | 2,018 |
SING: Symbol-to-Instrument Neural Generator | 60 | neurips | 25 | 1 | 2023-06-15 17:54:55.412000 | https://github.com/facebookresearch/SING | 155 | Sing: Symbol-to-instrument neural generator | https://scholar.google.com/scholar?cluster=9576037029701279224&hl=en&as_sdt=0,33 | 10 | 2,018 |
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks | 99 | neurips | 7 | 2 | 2023-06-15 17:54:55.602000 | https://github.com/sjblim/rmsn_nips_2018 | 30 | Forecasting treatment responses over time using recurrent marginal structural networks | https://scholar.google.com/scholar?cluster=9312966518414628527&hl=en&as_sdt=0,33 | 1 | 2,018 |
Quadratic Decomposable Submodular Function Minimization | 13 | neurips | 1 | 0 | 2023-06-15 17:54:55.793000 | https://github.com/lipan00123/QDSDM | 0 | Quadratic decomposable submodular function minimization | https://scholar.google.com/scholar?cluster=9668278212333240026&hl=en&as_sdt=0,33 | 1 | 2,018 |
Deep Anomaly Detection Using Geometric Transformations | 520 | neurips | 35 | 2 | 2023-06-15 17:54:55.983000 | https://github.com/izikgo/AnomalyDetectionTransformations | 154 | Deep anomaly detection using geometric transformations | https://scholar.google.com/scholar?cluster=15277146675093535725&hl=en&as_sdt=0,3 | 7 | 2,018 |
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation | 88 | neurips | 1 | 0 | 2023-06-15 17:54:56.174000 | https://github.com/MeckyWu/subspace-match | 15 | Towards understanding learning representations: To what extent do different neural networks learn the same representation | https://scholar.google.com/scholar?cluster=401428033641216502&hl=en&as_sdt=0,13 | 2 | 2,018 |
An intriguing failing of convolutional neural networks and the CoordConv solution | 730 | neurips | 37 | 7 | 2023-06-15 17:54:56.365000 | https://github.com/uber-research/coordconv | 202 | An intriguing failing of convolutional neural networks and the coordconv solution | https://scholar.google.com/scholar?cluster=1725137104710452960&hl=en&as_sdt=0,18 | 6 | 2,018 |
A Smoother Way to Train Structured Prediction Models | 18 | neurips | 4 | 0 | 2023-06-15 17:54:56.555000 | https://github.com/krishnap25/casimir | 2 | A smoother way to train structured prediction models | https://scholar.google.com/scholar?cluster=9176087356126828757&hl=en&as_sdt=0,10 | 2 | 2,018 |
3D-Aware Scene Manipulation via Inverse Graphics | 176 | neurips | 41 | 0 | 2023-06-15 17:54:56.746000 | https://github.com/ysymyth/3D-SDN | 262 | 3d-aware scene manipulation via inverse graphics | https://scholar.google.com/scholar?cluster=1601238761105816866&hl=en&as_sdt=0,44 | 16 | 2,018 |
Complex Gated Recurrent Neural Networks | 51 | neurips | 11 | 0 | 2023-06-15 17:54:56.936000 | https://github.com/v0lta/Complex-gated-recurrent-neural-networks | 42 | Complex gated recurrent neural networks | https://scholar.google.com/scholar?cluster=10862653902258650151&hl=en&as_sdt=0,11 | 3 | 2,018 |
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation | 191 | neurips | 425 | 18 | 2023-06-15 17:54:57.127000 | https://github.com/SullyChen/Autopilot-TensorFlow | 1,235 | Scalable end-to-end autonomous vehicle testing via rare-event simulation | https://scholar.google.com/scholar?cluster=5564001038044175212&hl=en&as_sdt=0,43 | 75 | 2,018 |
Learning Loop Invariants for Program Verification | 115 | neurips | 23 | 1 | 2023-06-15 17:54:57.318000 | https://github.com/PL-ML/code2inv | 74 | Learning loop invariants for program verification | https://scholar.google.com/scholar?cluster=6954633128371638771&hl=en&as_sdt=0,5 | 9 | 2,018 |
How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective | 158 | neurips | 1 | 1 | 2023-06-15 17:54:57.508000 | https://github.com/leiwu1990/sgd.stability | 10 | How sgd selects the global minima in over-parameterized learning: A dynamical stability perspective | https://scholar.google.com/scholar?cluster=1980119340021099329&hl=en&as_sdt=0,33 | 2 | 2,018 |
Neural Guided Constraint Logic Programming for Program Synthesis | 35 | neurips | 9 | 0 | 2023-06-15 17:54:57.698000 | https://github.com/xuexue/neuralkanren | 85 | Neural guided constraint logic programming for program synthesis | https://scholar.google.com/scholar?cluster=5770275785272500195&hl=en&as_sdt=0,36 | 9 | 2,018 |
Neural Ordinary Differential Equations | 3,210 | neurips | 848 | 61 | 2023-06-15 17:54:57.888000 | https://github.com/rtqichen/torchdiffeq | 4,672 | Neural ordinary differential equations | https://scholar.google.com/scholar?cluster=13748354740225969894&hl=en&as_sdt=0,33 | 123 | 2,018 |
Coupled Variational Bayes via Optimization Embedding | 29 | neurips | 3 | 2 | 2023-06-15 17:54:58.079000 | https://github.com/Hanjun-Dai/cvb | 10 | Coupled variational bayes via optimization embedding | https://scholar.google.com/scholar?cluster=9010555957492755231&hl=en&as_sdt=0,5 | 5 | 2,018 |
Policy Optimization via Importance Sampling | 87 | neurips | 3 | 1 | 2023-06-15 17:54:58.271000 | https://github.com/T3p/pois | 12 | Policy optimization via importance sampling | https://scholar.google.com/scholar?cluster=16130728419946747088&hl=en&as_sdt=0,5 | 6 | 2,018 |
Task-Driven Convolutional Recurrent Models of the Visual System | 144 | neurips | 15 | 0 | 2023-06-15 17:54:58.479000 | https://github.com/neuroailab/tnn | 92 | Task-driven convolutional recurrent models of the visual system | https://scholar.google.com/scholar?cluster=11039722383223148947&hl=en&as_sdt=0,34 | 12 | 2,018 |
Paraphrasing Complex Network: Network Compression via Factor Transfer | 374 | neurips | 7 | 0 | 2023-06-15 17:54:58.670000 | https://github.com/Jangho-Kim/Factor-Transfer-pytorch | 14 | Paraphrasing complex network: Network compression via factor transfer | https://scholar.google.com/scholar?cluster=2520473274058783123&hl=en&as_sdt=0,5 | 1 | 2,018 |
A Simple Cache Model for Image Recognition | 20 | neurips | 0 | 0 | 2023-06-15 17:54:58.861000 | https://github.com/eminorhan/simple-cache | 2 | A simple cache model for image recognition | https://scholar.google.com/scholar?cluster=3091315690960335000&hl=en&as_sdt=0,29 | 3 | 2,018 |
Learning Attractor Dynamics for Generative Memory | 20 | neurips | 16 | 0 | 2023-06-15 17:54:59.051000 | https://github.com/deepmind/dynamic-kanerva-machines | 40 | Learning attractor dynamics for generative memory | https://scholar.google.com/scholar?cluster=9940290258944118765&hl=en&as_sdt=0,22 | 11 | 2,018 |
Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior | 96 | neurips | 2 | 0 | 2023-06-15 17:54:59.241000 | https://github.com/rddy/isql | 27 | Where do you think you're going?: Inferring beliefs about dynamics from behavior | https://scholar.google.com/scholar?cluster=11438620297016616954&hl=en&as_sdt=0,22 | 6 | 2,018 |
Image Inpainting via Generative Multi-column Convolutional Neural Networks | 291 | neurips | 91 | 25 | 2023-06-15 17:54:59.431000 | https://github.com/shepnerd/inpainting_gmcnn | 400 | Image inpainting via generative multi-column convolutional neural networks | https://scholar.google.com/scholar?cluster=14919715529082387957&hl=en&as_sdt=0,40 | 20 | 2,018 |
A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices | 29 | neurips | 3 | 0 | 2023-06-15 17:54:59.622000 | https://github.com/zhenxingjian/SPD-SRU | 14 | A statistical recurrent model on the manifold of symmetric positive definite matrices | https://scholar.google.com/scholar?cluster=5544428600595730510&hl=en&as_sdt=0,44 | 2 | 2,018 |
Object-Oriented Dynamics Predictor | 31 | neurips | 3 | 0 | 2023-06-15 17:54:59.813000 | https://github.com/mig-zh/OODP | 13 | Object-oriented dynamics predictor | https://scholar.google.com/scholar?cluster=1811390955386289421&hl=en&as_sdt=0,11 | 3 | 2,018 |
To Trust Or Not To Trust A Classifier | 387 | neurips | 46 | 2 | 2023-06-15 17:55:00.003000 | https://github.com/google/TrustScore | 167 | To trust or not to trust a classifier | https://scholar.google.com/scholar?cluster=9292152849001694574&hl=en&as_sdt=0,10 | 14 | 2,018 |
Deep Reinforcement Learning of Marked Temporal Point Processes | 97 | neurips | 18 | 1 | 2023-06-15 17:55:00.194000 | https://github.com/Networks-Learning/tpprl | 71 | Deep reinforcement learning of marked temporal point processes | https://scholar.google.com/scholar?cluster=10991436220054749409&hl=en&as_sdt=0,30 | 7 | 2,018 |
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization | 146 | neurips | 10 | 0 | 2023-06-15 17:55:00.384000 | https://github.com/bargavj/distributedMachineLearning | 25 | Distributed learning without distress: Privacy-preserving empirical risk minimization | https://scholar.google.com/scholar?cluster=10577380829443665980&hl=en&as_sdt=0,38 | 0 | 2,018 |
Hybrid Knowledge Routed Modules for Large-scale Object Detection | 74 | neurips | 19 | 15 | 2023-06-15 17:55:00.574000 | https://github.com/chanyn/HKRM | 98 | Hybrid knowledge routed modules for large-scale object detection | https://scholar.google.com/scholar?cluster=18227077982790889117&hl=en&as_sdt=0,5 | 9 | 2,018 |
BRITS: Bidirectional Recurrent Imputation for Time Series | 394 | neurips | 67 | 12 | 2023-06-15 17:55:00.764000 | https://github.com/caow13/BRITS | 173 | Brits: Bidirectional recurrent imputation for time series | https://scholar.google.com/scholar?cluster=17928129084181066672&hl=en&as_sdt=0,47 | 6 | 2,018 |
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects | 222 | neurips | 28 | 1 | 2023-06-15 17:55:00.955000 | https://github.com/akosiorek/sqair | 96 | Sequential attend, infer, repeat: Generative modelling of moving objects | https://scholar.google.com/scholar?cluster=7430884807828197721&hl=en&as_sdt=0,11 | 11 | 2,018 |
Boosting Black Box Variational Inference | 26 | neurips | 5 | 0 | 2023-06-15 17:55:01.145000 | https://github.com/ratschlab/boosting-bbvi | 7 | Boosting black box variational inference | https://scholar.google.com/scholar?cluster=493456481295082921&hl=en&as_sdt=0,43 | 4 | 2,018 |
Transfer of Deep Reactive Policies for MDP Planning | 23 | neurips | 1 | 0 | 2023-06-15 17:55:01.337000 | https://github.com/dair-iitd/torpido | 7 | Transfer of deep reactive policies for mdp planning | https://scholar.google.com/scholar?cluster=4580400729732661142&hl=en&as_sdt=0,10 | 4 | 2,018 |
GILBO: One Metric to Measure Them All | 16 | neurips | 322 | 16 | 2023-06-15 17:55:01.527000 | https://github.com/google/compare_gan | 1,814 | GILBO: One metric to measure them all | https://scholar.google.com/scholar?cluster=14349686696431672115&hl=en&as_sdt=0,11 | 52 | 2,018 |
FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction | 103 | neurips | 94 | 0 | 2023-06-15 17:55:01.718000 | https://github.com/kevin-ssy/FishNet | 545 | Fishnet: A versatile backbone for image, region, and pixel level prediction | https://scholar.google.com/scholar?cluster=8077266557125333363&hl=en&as_sdt=0,33 | 23 | 2,018 |
Automatic differentiation in ML: Where we are and where we should be going | 75 | neurips | 43 | 31 | 2023-06-15 17:55:01.909000 | https://github.com/mila-udem/myia | 454 | Automatic differentiation in ML: Where we are and where we should be going | https://scholar.google.com/scholar?cluster=11874990560582038809&hl=en&as_sdt=0,3 | 31 | 2,018 |
Evolved Policy Gradients | 228 | neurips | 56 | 7 | 2023-06-15 17:55:02.100000 | https://github.com/openai/EPG | 240 | Evolved policy gradients | https://scholar.google.com/scholar?cluster=17605986776756195620&hl=en&as_sdt=0,36 | 14 | 2,018 |
Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features | 33 | neurips | 0 | 0 | 2023-06-15 17:55:02.291000 | https://github.com/r3831/SAKPCA | 0 | Streaming Kernel PCA with Random Features | https://scholar.google.com/scholar?cluster=17070435901311007360&hl=en&as_sdt=0,32 | 3 | 2,018 |
Faster Neural Networks Straight from JPEG | 175 | neurips | 40 | 12 | 2023-06-15 17:55:02.491000 | https://github.com/uber-research/jpeg2dct | 231 | Faster neural networks straight from jpeg | https://scholar.google.com/scholar?cluster=9617446820670115100&hl=en&as_sdt=0,5 | 11 | 2,018 |
Visual Reinforcement Learning with Imagined Goals | 456 | neurips | 520 | 39 | 2023-06-15 17:55:02.685000 | https://github.com/vitchyr/rlkit | 2,161 | Visual reinforcement learning with imagined goals | https://scholar.google.com/scholar?cluster=5007292417648560707&hl=en&as_sdt=0,8 | 61 | 2,018 |
Deep Generative Models for Distribution-Preserving Lossy Compression | 103 | neurips | 8 | 2 | 2023-06-15 17:55:02.879000 | https://github.com/mitscha/dplc | 34 | Deep generative models for distribution-preserving lossy compression | https://scholar.google.com/scholar?cluster=10590142637711882209&hl=en&as_sdt=0,14 | 3 | 2,018 |
With Friends Like These, Who Needs Adversaries? | 74 | neurips | 0 | 0 | 2023-06-15 17:55:03.072000 | https://github.com/torrvision/whoneedsadversaries | 12 | With friends like these, who needs adversaries? | https://scholar.google.com/scholar?cluster=5740676327222968631&hl=en&as_sdt=0,10 | 9 | 2,018 |
Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation | 72 | neurips | 19 | 10 | 2023-06-15 17:55:03.266000 | https://github.com/thusiyuan/cooperative_scene_parsing | 91 | Cooperative holistic scene understanding: Unifying 3d object, layout, and camera pose estimation | https://scholar.google.com/scholar?cluster=5227625249975009897&hl=en&as_sdt=0,5 | 5 | 2,018 |
Empirical Risk Minimization Under Fairness Constraints | 383 | neurips | 6 | 0 | 2023-06-15 17:55:03.460000 | https://github.com/jmikko/fair_ERM | 36 | Empirical risk minimization under fairness constraints | https://scholar.google.com/scholar?cluster=5746250113194301793&hl=en&as_sdt=0,5 | 3 | 2,018 |
A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation | 284 | neurips | 27 | 4 | 2023-06-15 17:55:03.654000 | https://github.com/Alexander-H-Liu/UFDN | 131 | A unified feature disentangler for multi-domain image translation and manipulation | https://scholar.google.com/scholar?cluster=6007789913986445498&hl=en&as_sdt=0,48 | 6 | 2,018 |
The committee machine: Computational to statistical gaps in learning a two-layers neural network | 79 | neurips | 0 | 0 | 2023-06-15 17:55:03.848000 | https://github.com/benjaminaubin/TheCommitteeMachine | 0 | The committee machine: Computational to statistical gaps in learning a two-layers neural network | https://scholar.google.com/scholar?cluster=4903323524016093175&hl=en&as_sdt=0,34 | 2 | 2,018 |
Evolution-Guided Policy Gradient in Reinforcement Learning | 183 | neurips | 52 | 2 | 2023-06-15 17:55:04.042000 | https://github.com/ShawK91/erl_paper_nips18 | 172 | Evolution-guided policy gradient in reinforcement learning | https://scholar.google.com/scholar?cluster=7920725821302044195&hl=en&as_sdt=0,10 | 5 | 2,018 |
Causal Inference with Noisy and Missing Covariates via Matrix Factorization | 61 | neurips | 0 | 0 | 2023-06-15 17:55:04.236000 | https://github.com/udellgroup/causal_mf_code | 6 | Causal inference with noisy and missing covariates via matrix factorization | https://scholar.google.com/scholar?cluster=14104978633422349618&hl=en&as_sdt=0,7 | 3 | 2,018 |
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation | 28 | neurips | 0 | 0 | 2023-06-15 17:55:04.430000 | https://github.com/jingnantes/hybrid-mst | 8 | Hybrid-MST: A hybrid active sampling strategy for pairwise preference aggregation | https://scholar.google.com/scholar?cluster=13558401002999071074&hl=en&as_sdt=0,10 | 2 | 2,018 |
A no-regret generalization of hierarchical softmax to extreme multi-label classification | 81 | neurips | 16 | 6 | 2023-06-15 17:55:04.624000 | https://github.com/mwydmuch/extremeText | 147 | A no-regret generalization of hierarchical softmax to extreme multi-label classification | https://scholar.google.com/scholar?cluster=14171307998042582918&hl=en&as_sdt=0,3 | 13 | 2,018 |
Rectangular Bounding Process | 21 | neurips | 3 | 0 | 2023-06-15 17:55:04.818000 | https://github.com/xuhuifan/RBP | 3 | Rectangular bounding process | https://scholar.google.com/scholar?cluster=10618275895500216203&hl=en&as_sdt=0,34 | 1 | 2,018 |
Constructing Unrestricted Adversarial Examples with Generative Models | 240 | neurips | 15 | 6 | 2023-06-15 17:55:05.011000 | https://github.com/ermongroup/generative_adversary | 60 | Constructing unrestricted adversarial examples with generative models | https://scholar.google.com/scholar?cluster=14086270849571978699&hl=en&as_sdt=0,39 | 6 | 2,018 |
Boosted Sparse and Low-Rank Tensor Regression | 32 | neurips | 4 | 3 | 2023-06-15 17:55:05.208000 | https://github.com/LifangHe/SURF | 9 | Boosted sparse and low-rank tensor regression | https://scholar.google.com/scholar?cluster=13402681948996325867&hl=en&as_sdt=0,10 | 3 | 2,018 |
Deep Neural Networks with Box Convolutions | 15 | neurips | 36 | 3 | 2023-06-15 17:55:05.403000 | https://github.com/shrubb/box-convolutions | 513 | Deep neural networks with box convolutions | https://scholar.google.com/scholar?cluster=15004510562166029998&hl=en&as_sdt=0,43 | 17 | 2,018 |
Learning Compressed Transforms with Low Displacement Rank | 42 | neurips | 17 | 6 | 2023-06-15 17:55:05.594000 | https://github.com/HazyResearch/structured-nets | 57 | Learning compressed transforms with low displacement rank | https://scholar.google.com/scholar?cluster=8419515952370992696&hl=en&as_sdt=0,50 | 17 | 2,018 |
Deep Defense: Training DNNs with Improved Adversarial Robustness | 115 | neurips | 6 | 0 | 2023-06-15 17:55:05.784000 | https://github.com/ZiangYan/deepdefense.pytorch | 37 | Deep defense: Training dnns with improved adversarial robustness | https://scholar.google.com/scholar?cluster=6643757979178770669&hl=en&as_sdt=0,1 | 4 | 2,018 |
Large-Scale Stochastic Sampling from the Probability Simplex | 5 | neurips | 0 | 1 | 2023-06-15 17:55:05.975000 | https://github.com/jbaker92/scir | 2 | Large-scale stochastic sampling from the probability simplex | https://scholar.google.com/scholar?cluster=9892795582424041794&hl=en&as_sdt=0,43 | 2 | 2,018 |
Adaptive Methods for Nonconvex Optimization | 321 | neurips | 15 | 3 | 2023-06-15 17:55:06.166000 | https://github.com/stefan-it/nmt-en-vi | 51 | Adaptive methods for nonconvex optimization | https://scholar.google.com/scholar?cluster=13576720529696525340&hl=en&as_sdt=0,33 | 6 | 2,018 |
Compact Generalized Non-local Network | 166 | neurips | 41 | 0 | 2023-06-15 17:55:06.357000 | https://github.com/KaiyuYue/cgnl-network.pytorch | 259 | Compact generalized non-local network | https://scholar.google.com/scholar?cluster=12004705320658184806&hl=en&as_sdt=0,5 | 7 | 2,018 |
Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning | 124 | neurips | 4 | 2 | 2023-06-15 17:55:06.548000 | https://github.com/ylytju/sga | 20 | Stacked semantics-guided attention model for fine-grained zero-shot learning | https://scholar.google.com/scholar?cluster=17870706793172229300&hl=en&as_sdt=0,10 | 1 | 2,018 |
Banach Wasserstein GAN | 214 | neurips | 10 | 1 | 2023-06-15 17:55:06.739000 | https://github.com/adler-j/bwgan | 31 | Banach wasserstein gan | https://scholar.google.com/scholar?cluster=10419609167162928003&hl=en&as_sdt=0,5 | 6 | 2,018 |
Visual Object Networks: Image Generation with Disentangled 3D Representations | 203 | neurips | 91 | 12 | 2023-06-15 17:55:06.930000 | https://github.com/junyanz/VON | 530 | Visual object networks: Image generation with disentangled 3D representations | https://scholar.google.com/scholar?cluster=3404291286977602499&hl=en&as_sdt=0,5 | 32 | 2,018 |
MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare | 214 | neurips | 27 | 1 | 2023-06-15 17:55:07.120000 | https://github.com/mp2893/mime | 98 | Mime: Multilevel medical embedding of electronic health records for predictive healthcare | https://scholar.google.com/scholar?cluster=9778014794664384350&hl=en&as_sdt=0,47 | 7 | 2,018 |
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams | 74 | neurips | 0 | 1 | 2023-06-15 17:55:07.311000 | https://github.com/lttam/PersistenceFisher | 5 | Persistence fisher kernel: A riemannian manifold kernel for persistence diagrams | https://scholar.google.com/scholar?cluster=1409702383947125765&hl=en&as_sdt=0,5 | 2 | 2,018 |
Bilinear Attention Networks | 720 | neurips | 102 | 2 | 2023-06-15 17:55:07.501000 | https://github.com/jnhwkim/ban-vqa | 515 | Bilinear attention networks | https://scholar.google.com/scholar?cluster=10383181412923835294&hl=en&as_sdt=0,5 | 17 | 2,018 |
Constructing Fast Network through Deconstruction of Convolution | 67 | neurips | 5 | 0 | 2023-06-15 17:55:07.692000 | https://github.com/jyh2986/Active-Shift | 31 | Constructing fast network through deconstruction of convolution | https://scholar.google.com/scholar?cluster=15893085353567655931&hl=en&as_sdt=0,14 | 3 | 2,018 |
See and Think: Disentangling Semantic Scene Completion | 65 | neurips | 10 | 9 | 2023-06-15 17:55:07.882000 | https://github.com/ShiceLiu/SATNet | 47 | See and think: Disentangling semantic scene completion | https://scholar.google.com/scholar?cluster=3218225429355211096&hl=en&as_sdt=0,10 | 5 | 2,018 |
Unsupervised Depth Estimation, 3D Face Rotation and Replacement | 31 | neurips | 31 | 6 | 2023-06-15 17:55:08.073000 | https://github.com/joelmoniz/DepthNets | 124 | Unsupervised depth estimation, 3d face rotation and replacement | https://scholar.google.com/scholar?cluster=2371681385764042999&hl=en&as_sdt=0,44 | 8 | 2,018 |
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language | 14 | neurips | 7 | 2 | 2023-06-15 17:55:08.263000 | https://github.com/google-research/autoconj | 36 | Autoconj: recognizing and exploiting conjugacy without a domain-specific language | https://scholar.google.com/scholar?cluster=10948786372244458956&hl=en&as_sdt=0,34 | 11 | 2,018 |
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance | 78 | neurips | 26 | 2 | 2023-06-15 17:55:08.454000 | https://github.com/ermongroup/ssdkl | 65 | Semi-supervised deep kernel learning: Regression with unlabeled data by minimizing predictive variance | https://scholar.google.com/scholar?cluster=6491716866958005670&hl=en&as_sdt=0,35 | 8 | 2,018 |
Stimulus domain transfer in recurrent models for large scale cortical population prediction on video | 44 | neurips | 8 | 0 | 2023-06-15 17:55:08.645000 | https://github.com/sinzlab/Sinz2018_NIPS | 3 | Stimulus domain transfer in recurrent models for large scale cortical population prediction on video | https://scholar.google.com/scholar?cluster=3426947555786993703&hl=en&as_sdt=0,5 | 3 | 2,018 |
Norm matters: efficient and accurate normalization schemes in deep networks | 166 | neurips | 3 | 0 | 2023-06-15 17:55:08.835000 | https://github.com/eladhoffer/norm_matters | 22 | Norm matters: efficient and accurate normalization schemes in deep networks | https://scholar.google.com/scholar?cluster=12023191299459902610&hl=en&as_sdt=0,29 | 4 | 2,018 |
Dialog-based Interactive Image Retrieval | 145 | neurips | 19 | 4 | 2023-06-15 17:55:09.026000 | https://github.com/XiaoxiaoGuo/fashion-retrieval | 66 | Dialog-based interactive image retrieval | https://scholar.google.com/scholar?cluster=4258300372823907612&hl=en&as_sdt=0,36 | 4 | 2,018 |
Co-teaching: Robust training of deep neural networks with extremely noisy labels | 1,450 | neurips | 98 | 9 | 2023-06-15 17:55:09.217000 | https://github.com/bhanML/Co-teaching | 447 | Co-teaching: Robust training of deep neural networks with extremely noisy labels | https://scholar.google.com/scholar?cluster=1619874673011079691&hl=en&as_sdt=0,5 | 11 | 2,018 |
Learning to Reason with Third Order Tensor Products | 65 | neurips | 4 | 0 | 2023-06-15 17:55:09.407000 | https://github.com/ischlag/TPR-RNN | 39 | Learning to reason with third order tensor products | https://scholar.google.com/scholar?cluster=1859815740065749231&hl=en&as_sdt=0,43 | 4 | 2,018 |
Deep Structured Prediction with Nonlinear Output Transformations | 22 | neurips | 0 | 1 | 2023-06-15 17:55:09.597000 | https://github.com/cgraber/NLStruct | 11 | Deep structured prediction with nonlinear output transformations | https://scholar.google.com/scholar?cluster=14558697357825196777&hl=en&as_sdt=0,31 | 6 | 2,018 |
Visualizing the Loss Landscape of Neural Nets | 1,487 | neurips | 345 | 23 | 2023-06-15 17:55:09.787000 | https://github.com/tomgoldstein/loss-landscape | 2,379 | Visualizing the loss landscape of neural nets | https://scholar.google.com/scholar?cluster=11650483902238288010&hl=en&as_sdt=0,5 | 33 | 2,018 |
Representation Learning for Treatment Effect Estimation from Observational Data | 223 | neurips | 8 | 3 | 2023-06-15 17:55:09.979000 | https://github.com/Osier-Yi/SITE | 48 | Representation learning for treatment effect estimation from observational data | https://scholar.google.com/scholar?cluster=8473125110526248121&hl=en&as_sdt=0,14 | 2 | 2,018 |
Memory Replay GANs: Learning to Generate New Categories without Forgetting | 323 | neurips | 17 | 6 | 2023-06-15 17:55:10.169000 | https://github.com/WuChenshen/MeRGAN | 57 | Memory replay gans: Learning to generate new categories without forgetting | https://scholar.google.com/scholar?cluster=10386986757383440246&hl=en&as_sdt=0,5 | 2 | 2,018 |
Insights on representational similarity in neural networks with canonical correlation | 317 | neurips | 145 | 7 | 2023-06-15 17:55:10.360000 | https://github.com/google/svcca | 596 | Insights on representational similarity in neural networks with canonical correlation | https://scholar.google.com/scholar?cluster=15689105000424764079&hl=en&as_sdt=0,48 | 27 | 2,018 |
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network | 191 | neurips | 369 | 28 | 2023-06-15 17:55:10.552000 | https://github.com/Microsoft/EdgeML | 1,453 | Fastgrnn: A fast, accurate, stable and tiny kilobyte sized gated recurrent neural network | https://scholar.google.com/scholar?cluster=14286601091173970187&hl=en&as_sdt=0,41 | 87 | 2,018 |
Conditional Adversarial Domain Adaptation | 1,680 | neurips | 88 | 15 | 2023-06-15 17:55:10.742000 | https://github.com/thuml/CDAN | 374 | Conditional adversarial domain adaptation | https://scholar.google.com/scholar?cluster=951003799487024572&hl=en&as_sdt=0,5 | 11 | 2,018 |
Bayesian Nonparametric Spectral Estimation | 36 | neurips | 2 | 2 | 2023-06-15 17:55:10.933000 | https://github.com/GAMES-UChile/BayesianSpectralEstimation | 14 | Bayesian nonparametric spectral estimation | https://scholar.google.com/scholar?cluster=17785517224633397163&hl=en&as_sdt=0,5 | 4 | 2,018 |
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks | 1,316 | neurips | 78 | 16 | 2023-06-15 17:55:11.123000 | https://github.com/pokaxpoka/deep_Mahalanobis_detector | 303 | A simple unified framework for detecting out-of-distribution samples and adversarial attacks | https://scholar.google.com/scholar?cluster=59561906500021733&hl=en&as_sdt=0,31 | 9 | 2,018 |
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise | 464 | neurips | 15 | 0 | 2023-06-15 17:55:11.314000 | https://github.com/mmazeika/glc | 87 | Using trusted data to train deep networks on labels corrupted by severe noise | https://scholar.google.com/scholar?cluster=3616817429291706463&hl=en&as_sdt=0,44 | 4 | 2,018 |
Masking: A New Perspective of Noisy Supervision | 208 | neurips | 7 | 0 | 2023-06-15 17:55:11.505000 | https://github.com/bhanML/Masking | 55 | Masking: A new perspective of noisy supervision | https://scholar.google.com/scholar?cluster=10612946092230113975&hl=en&as_sdt=0,32 | 5 | 2,018 |
Found Graph Data and Planted Vertex Covers | 9 | neurips | 1 | 0 | 2023-06-15 17:55:11.695000 | https://github.com/arbenson/FGDnPVC | 3 | Found graph data and planted vertex covers | https://scholar.google.com/scholar?cluster=3952614015987874962&hl=en&as_sdt=0,39 | 3 | 2,018 |
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