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Fast Estimation of Causal Interactions using Wold Processes | 12 | neurips | 4 | 2 | 2023-06-15 17:55:11.886000 | https://github.com/flaviovdf/granger-busca | 6 | Fast estimation of causal interactions using wold processes | https://scholar.google.com/scholar?cluster=3436970798067835046&hl=en&as_sdt=0,44 | 3 | 2,018 |
Reparameterization Gradient for Non-differentiable Models | 25 | neurips | 1 | 0 | 2023-06-15 17:55:12.077000 | https://github.com/wonyeol/reparam-nondiff | 5 | Reparameterization gradient for non-differentiable models | https://scholar.google.com/scholar?cluster=15564293157719874680&hl=en&as_sdt=0,31 | 3 | 2,018 |
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents | 352 | neurips | 298 | 20 | 2023-06-15 17:55:12.267000 | https://github.com/uber-research/deep-neuroevolution | 1,597 | Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents | https://scholar.google.com/scholar?cluster=9461747331584701646&hl=en&as_sdt=0,11 | 82 | 2,018 |
Generalizing Tree Probability Estimation via Bayesian Networks | 23 | neurips | 6 | 0 | 2023-06-15 17:55:12.458000 | https://github.com/zcrabbit/sbn | 8 | Generalizing tree probability estimation via Bayesian networks | https://scholar.google.com/scholar?cluster=17096075908350325992&hl=en&as_sdt=0,5 | 1 | 2,018 |
SimplE Embedding for Link Prediction in Knowledge Graphs | 661 | neurips | 36 | 1 | 2023-06-15 17:55:12.648000 | https://github.com/Mehran-k/SimplE | 134 | Simple embedding for link prediction in knowledge graphs | https://scholar.google.com/scholar?cluster=1390081697322675650&hl=en&as_sdt=0,5 | 9 | 2,018 |
Statistical mechanics of low-rank tensor decomposition | 16 | neurips | 0 | 0 | 2023-06-15 17:55:12.839000 | https://github.com/ganguli-lab/tensorAMP | 4 | Statistical mechanics of low-rank tensor decomposition | https://scholar.google.com/scholar?cluster=9594213569092054865&hl=en&as_sdt=0,1 | 4 | 2,018 |
A Structured Prediction Approach for Label Ranking | 30 | neurips | 2 | 0 | 2023-06-15 17:55:13.030000 | https://github.com/akorba/Structured_Approach_Label_Ranking | 6 | A structured prediction approach for label ranking | https://scholar.google.com/scholar?cluster=7075820179073932212&hl=en&as_sdt=0,41 | 3 | 2,018 |
Sparsified SGD with Memory | 594 | neurips | 11 | 1 | 2023-06-15 17:55:13.221000 | https://github.com/epfml/sparsifiedSGD | 50 | Sparsified SGD with memory | https://scholar.google.com/scholar?cluster=6832257024596167334&hl=en&as_sdt=0,36 | 9 | 2,018 |
Model Agnostic Supervised Local Explanations | 167 | neurips | 8 | 0 | 2023-06-15 17:55:13.411000 | https://github.com/GDPlumb/MAPLE | 26 | Model agnostic supervised local explanations | https://scholar.google.com/scholar?cluster=3090118674779699868&hl=en&as_sdt=0,23 | 3 | 2,018 |
Probabilistic Matrix Factorization for Automated Machine Learning | 126 | neurips | 13 | 4 | 2023-06-15 17:55:13.601000 | https://github.com/rsheth80/pmf-automl | 41 | Probabilistic matrix factorization for automated machine learning | https://scholar.google.com/scholar?cluster=6902330776298089199&hl=en&as_sdt=0,21 | 4 | 2,018 |
Norm-Ranging LSH for Maximum Inner Product Search | 47 | neurips | 10 | 0 | 2023-06-15 17:55:13.792000 | https://github.com/xinyandai/similarity-search | 18 | Norm-ranging lsh for maximum inner product search | https://scholar.google.com/scholar?cluster=4956999863940081632&hl=en&as_sdt=0,47 | 10 | 2,018 |
Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions | 85 | neurips | 2 | 0 | 2023-06-15 17:55:13.983000 | https://github.com/BorisMuzellec/EllipticalEmbeddings | 9 | Generalizing point embeddings using the wasserstein space of elliptical distributions | https://scholar.google.com/scholar?cluster=3601826070675882278&hl=en&as_sdt=0,23 | 4 | 2,018 |
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification | 60 | neurips | 2 | 0 | 2023-06-15 17:55:14.173000 | https://github.com/dmilios/dirichletGPC | 13 | Dirichlet-based gaussian processes for large-scale calibrated classification | https://scholar.google.com/scholar?cluster=7488422957804807823&hl=en&as_sdt=0,36 | 2 | 2,018 |
Latent Alignment and Variational Attention | 138 | neurips | 60 | 2 | 2023-06-15 17:55:14.363000 | https://github.com/harvardnlp/var-attn | 324 | Latent alignment and variational attention | https://scholar.google.com/scholar?cluster=6335407498429393003&hl=en&as_sdt=0,37 | 23 | 2,018 |
Infinite-Horizon Gaussian Processes | 29 | neurips | 7 | 3 | 2023-06-15 17:55:14.554000 | https://github.com/AaltoML/IHGP | 28 | Infinite-horizon Gaussian processes | https://scholar.google.com/scholar?cluster=13722784833220822191&hl=en&as_sdt=0,5 | 6 | 2,018 |
Constrained Graph Variational Autoencoders for Molecule Design | 405 | neurips | 54 | 4 | 2023-06-15 17:55:14.744000 | https://github.com/Microsoft/constrained-graph-variational-autoencoder | 202 | Constrained graph variational autoencoders for molecule design | https://scholar.google.com/scholar?cluster=2838800553083041205&hl=en&as_sdt=0,23 | 11 | 2,018 |
Hardware Conditioned Policies for Multi-Robot Transfer Learning | 65 | neurips | 7 | 0 | 2023-06-15 17:55:14.935000 | https://github.com/taochenshh/hcp | 17 | Hardware conditioned policies for multi-robot transfer learning | https://scholar.google.com/scholar?cluster=11432360308578824406&hl=en&as_sdt=0,33 | 4 | 2,018 |
Learning Disentangled Joint Continuous and Discrete Representations | 203 | neurips | 65 | 1 | 2023-06-15 17:55:15.125000 | https://github.com/Schlumberger/joint-vae | 449 | Learning disentangled joint continuous and discrete representations | https://scholar.google.com/scholar?cluster=14996308996785863098&hl=en&as_sdt=0,10 | 21 | 2,018 |
Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples | 158 | neurips | 6 | 1 | 2023-06-15 17:55:15.316000 | https://github.com/AmIAttribute/AmI | 29 | Attacks meet interpretability: Attribute-steered detection of adversarial samples | https://scholar.google.com/scholar?cluster=2985314933504776828&hl=en&as_sdt=0,5 | 1 | 2,018 |
Differentiable MPC for End-to-end Planning and Control | 286 | neurips | 42 | 4 | 2023-06-15 17:55:15.506000 | https://github.com/locuslab/differentiable-mpc | 157 | Differentiable mpc for end-to-end planning and control | https://scholar.google.com/scholar?cluster=14843462917652881335&hl=en&as_sdt=0,43 | 10 | 2,018 |
Binary Classification from Positive-Confidence Data | 58 | neurips | 6 | 0 | 2023-06-15 17:55:15.697000 | https://github.com/takashiishida/pconf | 50 | Binary classification from positive-confidence data | https://scholar.google.com/scholar?cluster=10725870998628923240&hl=en&as_sdt=0,33 | 7 | 2,018 |
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs | 492 | neurips | 39 | 1 | 2023-06-15 17:55:15.887000 | https://github.com/timgaripov/dnn-mode-connectivity | 217 | Loss surfaces, mode connectivity, and fast ensembling of dnns | https://scholar.google.com/scholar?cluster=7857512178594187445&hl=en&as_sdt=0,1 | 12 | 2,018 |
A Unified View of Piecewise Linear Neural Network Verification | 294 | neurips | 8 | 0 | 2023-06-15 17:55:16.078000 | https://github.com/oval-group/PLNN-verification | 33 | A unified view of piecewise linear neural network verification | https://scholar.google.com/scholar?cluster=5109084814333031747&hl=en&as_sdt=0,22 | 9 | 2,018 |
Can We Gain More from Orthogonality Regularizations in Training Deep Networks? | 284 | neurips | 28 | 0 | 2023-06-15 17:55:16.268000 | https://github.com/nbansal90/Can-we-Gain-More-from-Orthogonality | 113 | Can we gain more from orthogonality regularizations in training deep networks? | https://scholar.google.com/scholar?cluster=16253012284749788151&hl=en&as_sdt=0,33 | 9 | 2,018 |
Training deep learning based denoisers without ground truth data | 114 | neurips | 11 | 0 | 2023-06-15 17:55:16.459000 | https://github.com/Shakarim94/Net-SURE | 43 | Training deep learning based denoisers without ground truth data | https://scholar.google.com/scholar?cluster=10949844547317882495&hl=en&as_sdt=0,33 | 2 | 2,018 |
Structural Causal Bandits: Where to Intervene? | 74 | neurips | 10 | 0 | 2023-06-15 17:55:16.649000 | https://github.com/sanghack81/SCMMAB-NIPS2018 | 16 | Structural causal bandits: Where to intervene? | https://scholar.google.com/scholar?cluster=4413359648093381122&hl=en&as_sdt=0,5 | 1 | 2,018 |
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms | 964 | neurips | 98 | 8 | 2023-06-15 17:55:16.840000 | https://github.com/brain-research/realistic-ssl-evaluation | 448 | Realistic evaluation of deep semi-supervised learning algorithms | https://scholar.google.com/scholar?cluster=15456844754123849487&hl=en&as_sdt=0,19 | 43 | 2,018 |
Revisiting Decomposable Submodular Function Minimization with Incidence Relations | 22 | neurips | 1 | 0 | 2023-06-15 17:55:17.031000 | https://github.com/lipan00123/DSFM-with-incidence-relations | 0 | Revisiting decomposable submodular function minimization with incidence relations | https://scholar.google.com/scholar?cluster=11168625649110015445&hl=en&as_sdt=0,25 | 1 | 2,018 |
Scaling Gaussian Process Regression with Derivatives | 79 | neurips | 8 | 4 | 2023-06-15 17:55:17.221000 | https://github.com/ericlee0803/GP_Derivatives | 31 | Scaling Gaussian process regression with derivatives | https://scholar.google.com/scholar?cluster=12933093226685125068&hl=en&as_sdt=0,33 | 11 | 2,018 |
FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification | 327 | neurips | 80 | 10 | 2023-06-15 17:55:17.411000 | https://github.com/yxgeee/FD-GAN | 275 | Fd-gan: Pose-guided feature distilling gan for robust person re-identification | https://scholar.google.com/scholar?cluster=8848217033553196180&hl=en&as_sdt=0,1 | 8 | 2,018 |
Graphical Generative Adversarial Networks | 41 | neurips | 15 | 4 | 2023-06-15 17:55:17.602000 | https://github.com/zhenxuan00/graphical-gan | 71 | Graphical generative adversarial networks | https://scholar.google.com/scholar?cluster=13094733406106291079&hl=en&as_sdt=0,29 | 14 | 2,018 |
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives | 488 | neurips | 15 | 0 | 2023-06-15 17:55:17.792000 | https://github.com/IBM/Contrastive-Explanation-Method | 51 | Explanations based on the missing: Towards contrastive explanations with pertinent negatives | https://scholar.google.com/scholar?cluster=14566322531022731329&hl=en&as_sdt=0,39 | 13 | 2,018 |
Context-aware Synthesis and Placement of Object Instances | 94 | neurips | 10 | 6 | 2023-06-15 17:55:17.983000 | https://github.com/NVlabs/Instance_Insertion | 84 | Context-aware synthesis and placement of object instances | https://scholar.google.com/scholar?cluster=16175327312247199712&hl=en&as_sdt=0,31 | 17 | 2,018 |
Group Equivariant Capsule Networks | 119 | neurips | 9 | 5 | 2023-06-15 17:55:18.174000 | https://github.com/mrjel/group_equivariant_capsules_pytorch | 29 | Group equivariant capsule networks | https://scholar.google.com/scholar?cluster=11608023930229611825&hl=en&as_sdt=0,10 | 2 | 2,018 |
MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval | 5 | neurips | 2 | 0 | 2023-06-15 17:55:18.364000 | https://github.com/epfl-lts2/mulan | 1 | Mulan: A blind and off-grid method for multichannel echo retrieval | https://scholar.google.com/scholar?cluster=88608764706264858&hl=en&as_sdt=0,5 | 9 | 2,018 |
Breaking the Activation Function Bottleneck through Adaptive Parameterization | 12 | neurips | 5 | 1 | 2023-06-15 17:55:18.554000 | https://github.com/flennerhag/alstm | 25 | Breaking the activation function bottleneck through adaptive parameterization | https://scholar.google.com/scholar?cluster=707894120541881868&hl=en&as_sdt=0,5 | 2 | 2,018 |
Topkapi: Parallel and Fast Sketches for Finding Top-K Frequent Elements | 11 | neurips | 0 | 0 | 2023-06-15 17:55:18.745000 | https://github.com/ankushmandal/topkapi | 11 | Topkapi: parallel and fast sketches for finding top-k frequent elements | https://scholar.google.com/scholar?cluster=17308935081714564523&hl=en&as_sdt=0,26 | 2 | 2,018 |
The Price of Fair PCA: One Extra dimension | 118 | neurips | 15 | 1 | 2023-06-15 17:55:18.935000 | https://github.com/samirasamadi/Fair-PCA | 23 | The price of fair pca: One extra dimension | https://scholar.google.com/scholar?cluster=6814300972813312615&hl=en&as_sdt=0,30 | 4 | 2,018 |
Orthogonally Decoupled Variational Gaussian Processes | 43 | neurips | 1 | 0 | 2023-06-15 17:55:19.125000 | https://github.com/hughsalimbeni/orth_decoupled_var_gps | 12 | Orthogonally decoupled variational Gaussian processes | https://scholar.google.com/scholar?cluster=13926573353559028690&hl=en&as_sdt=0,47 | 4 | 2,018 |
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation | 22 | neurips | 0 | 0 | 2023-06-15 17:55:19.316000 | https://github.com/shivapratap/AlgorithmicAssurance_NIPS2018 | 3 | Algorithmic assurance: An active approach to algorithmic testing using bayesian optimisation | https://scholar.google.com/scholar?cluster=6517267723562437007&hl=en&as_sdt=0,15 | 1 | 2,018 |
Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds | 204 | neurips | 23 | 2 | 2023-06-15 17:55:19.508000 | https://github.com/xchen-tamu/linear-lista-cpss | 48 | Theoretical linear convergence of unfolded ISTA and its practical weights and thresholds | https://scholar.google.com/scholar?cluster=8395828592719058096&hl=en&as_sdt=0,5 | 5 | 2,018 |
Efficient Neural Network Robustness Certification with General Activation Functions | 580 | neurips | 6 | 0 | 2023-06-15 17:55:19.699000 | https://github.com/huanzhang12/CROWN-Robustness-Certification | 13 | Efficient neural network robustness certification with general activation functions | https://scholar.google.com/scholar?cluster=6606953928208344058&hl=en&as_sdt=0,44 | 4 | 2,018 |
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning | 86 | neurips | 9 | 4 | 2023-06-15 17:55:19.889000 | https://github.com/tylersco/adapted_deep_embeddings | 26 | Adapted deep embeddings: A synthesis of methods for k-shot inductive transfer learning | https://scholar.google.com/scholar?cluster=11224359097846918125&hl=en&as_sdt=0,14 | 4 | 2,018 |
KONG: Kernels for ordered-neighborhood graphs | 3 | neurips | 2 | 0 | 2023-06-15 17:55:20.080000 | https://github.com/kokiche/KONG | 8 | KONG: Kernels for ordered-neighborhood graphs | https://scholar.google.com/scholar?cluster=7783420986460591653&hl=en&as_sdt=0,6 | 2 | 2,018 |
Glow: Generative Flow with Invertible 1x1 Convolutions | 2,412 | neurips | 509 | 64 | 2023-06-15 17:55:20.270000 | https://github.com/openai/glow | 3,016 | Glow: Generative flow with invertible 1x1 convolutions | https://scholar.google.com/scholar?cluster=5834689841973227263&hl=en&as_sdt=0,5 | 212 | 2,018 |
Efficient Projection onto the Perfect Phylogeny Model | 4 | neurips | 1 | 0 | 2023-06-15 17:55:20.461000 | https://github.com/bentoayr/Efficient-Projection-onto-the-Perfect-Phylogeny-Model | 2 | Efficient projection onto the perfect phylogeny model | https://scholar.google.com/scholar?cluster=5821955687711188887&hl=en&as_sdt=0,5 | 2 | 2,018 |
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient | 54 | neurips | 2 | 0 | 2023-06-15 17:55:20.651000 | https://github.com/aaronpmishkin/SLANG | 8 | Slang: Fast structured covariance approximations for bayesian deep learning with natural gradient | https://scholar.google.com/scholar?cluster=16145055537497825367&hl=en&as_sdt=0,47 | 4 | 2,018 |
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds | 29 | neurips | 2 | 0 | 2023-06-15 17:55:20.841000 | https://github.com/boschresearch/PAC_GP | 9 | Learning gaussian processes by minimizing pac-bayesian generalization bounds | https://scholar.google.com/scholar?cluster=10486427122061554310&hl=en&as_sdt=0,44 | 8 | 2,018 |
Lipschitz regularity of deep neural networks: analysis and efficient estimation | 369 | neurips | 14 | 3 | 2023-06-15 17:55:21.032000 | https://github.com/avirmaux/lipEstimation | 49 | Lipschitz regularity of deep neural networks: analysis and efficient estimation | https://scholar.google.com/scholar?cluster=16196721810320018514&hl=en&as_sdt=0,36 | 1 | 2,018 |
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation | 792 | neurips | 102 | 9 | 2023-06-15 17:55:21.222000 | https://github.com/bowenliu16/rl_graph_generation | 310 | Graph convolutional policy network for goal-directed molecular graph generation | https://scholar.google.com/scholar?cluster=15276529180320001334&hl=en&as_sdt=0,39 | 19 | 2,018 |
Video-to-Video Synthesis | 927 | neurips | 1,195 | 104 | 2023-06-15 17:55:21.413000 | https://github.com/NVIDIA/vid2vid | 8,266 | Video-to-video synthesis | https://scholar.google.com/scholar?cluster=3120460092236365926&hl=en&as_sdt=0,23 | 250 | 2,018 |
Bandit Learning with Implicit Feedback | 22 | neurips | 4 | 0 | 2023-06-15 17:55:21.604000 | https://github.com/qy7171/ec_bandit | 7 | Bandit learning with implicit feedback | https://scholar.google.com/scholar?cluster=11670456531413289871&hl=en&as_sdt=0,6 | 1 | 2,018 |
Adversarial Regularizers in Inverse Problems | 202 | neurips | 6 | 1 | 2023-06-15 17:55:21.794000 | https://github.com/lunz-s/DeepAdverserialRegulariser | 13 | Adversarial regularizers in inverse problems | https://scholar.google.com/scholar?cluster=3594915696133260277&hl=en&as_sdt=0,34 | 2 | 2,018 |
Hyperbolic Neural Networks | 411 | neurips | 26 | 3 | 2023-06-15 17:55:21.985000 | https://github.com/dalab/hyperbolic_nn | 162 | Hyperbolic neural networks | https://scholar.google.com/scholar?cluster=12122146629122312177&hl=en&as_sdt=0,31 | 14 | 2,018 |
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks | 492 | neurips | 31 | 9 | 2023-06-15 17:55:22.176000 | https://github.com/hujie-frank/GENet | 227 | Gather-excite: Exploiting feature context in convolutional neural networks | https://scholar.google.com/scholar?cluster=9719951211536151216&hl=en&as_sdt=0,5 | 19 | 2,018 |
Active Learning for Non-Parametric Regression Using Purely Random Trees | 21 | neurips | 3 | 0 | 2023-06-15 17:55:22.366000 | https://github.com/jackrgoetz/Mondrian_Tree_AL | 3 | Active learning for non-parametric regression using purely random trees | https://scholar.google.com/scholar?cluster=7681049792975239576&hl=en&as_sdt=0,44 | 4 | 2,018 |
Image-to-image translation for cross-domain disentanglement | 265 | neurips | 19 | 5 | 2023-06-15 17:55:22.557000 | https://github.com/agonzgarc/cross-domain-disen | 88 | Image-to-image translation for cross-domain disentanglement | https://scholar.google.com/scholar?cluster=7146735712017629088&hl=en&as_sdt=0,48 | 3 | 2,018 |
Practical Methods for Graph Two-Sample Testing | 36 | neurips | 2 | 0 | 2023-06-15 17:55:22.747000 | https://github.com/gdebarghya/Network-TwoSampleTesting | 5 | Practical methods for graph two-sample testing | https://scholar.google.com/scholar?cluster=3213877141900838189&hl=en&as_sdt=0,6 | 1 | 2,018 |
Learning to Navigate in Cities Without a Map | 279 | neurips | 56 | 4 | 2023-06-15 17:55:22.938000 | https://github.com/deepmind/streetlearn | 268 | Learning to navigate in cities without a map | https://scholar.google.com/scholar?cluster=9758707731169438744&hl=en&as_sdt=0,39 | 12 | 2,018 |
Invertibility of Convolutional Generative Networks from Partial Measurements | 79 | neurips | 2 | 1 | 2023-06-15 17:55:23.129000 | https://github.com/fangchangma/invert-generative-networks | 19 | Invertibility of convolutional generative networks from partial measurements | https://scholar.google.com/scholar?cluster=13691072756611951369&hl=en&as_sdt=0,19 | 4 | 2,018 |
Towards Robust Detection of Adversarial Examples | 184 | neurips | 11 | 0 | 2023-06-15 17:55:23.320000 | https://github.com/P2333/Reverse-Cross-Entropy | 41 | Towards robust detection of adversarial examples | https://scholar.google.com/scholar?cluster=12795339654045612460&hl=en&as_sdt=0,18 | 4 | 2,018 |
Direct Estimation of Differences in Causal Graphs | 24 | neurips | 0 | 0 | 2023-06-15 17:55:23.510000 | https://github.com/csquires/dci | 8 | Direct estimation of differences in causal graphs | https://scholar.google.com/scholar?cluster=6891353891081698977&hl=en&as_sdt=0,26 | 5 | 2,018 |
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments | 145 | neurips | 820 | 36 | 2023-06-15 17:55:23.701000 | https://github.com/deepmind/open_spiel | 3,694 | Actor-critic policy optimization in partially observable multiagent environments | https://scholar.google.com/scholar?cluster=8096003745039146783&hl=en&as_sdt=0,34 | 106 | 2,018 |
End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems | 305 | neurips | 428 | 52 | 2023-06-15 17:55:23.891000 | https://github.com/deepmodeling/deepmd-kit | 1,144 | End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems | https://scholar.google.com/scholar?cluster=4009423108945551834&hl=en&as_sdt=0,41 | 49 | 2,018 |
DAGs with NO TEARS: Continuous Optimization for Structure Learning | 501 | neurips | 111 | 5 | 2023-06-15 17:55:24.082000 | https://github.com/xunzheng/notears | 482 | Dags with no tears: Continuous optimization for structure learning | https://scholar.google.com/scholar?cluster=7128195536288105484&hl=en&as_sdt=0,36 | 21 | 2,018 |
Connectionist Temporal Classification with Maximum Entropy Regularization | 49 | neurips | 41 | 8 | 2023-06-15 17:55:24.273000 | https://github.com/liuhu-bigeye/enctc.crnn | 137 | Connectionist temporal classification with maximum entropy regularization | https://scholar.google.com/scholar?cluster=16455105685023612483&hl=en&as_sdt=0,5 | 10 | 2,018 |
Are GANs Created Equal? A Large-Scale Study | 994 | neurips | 322 | 16 | 2023-06-15 17:55:24.464000 | https://github.com/google/compare_gan | 1,814 | Are gans created equal? a large-scale study | https://scholar.google.com/scholar?cluster=3229217754457345915&hl=en&as_sdt=0,5 | 52 | 2,018 |
FRAGE: Frequency-Agnostic Word Representation | 149 | neurips | 21 | 6 | 2023-06-15 17:55:24.655000 | https://github.com/ChengyueGongR/FrequencyAgnostic | 117 | Frage: Frequency-agnostic word representation | https://scholar.google.com/scholar?cluster=899516517229807927&hl=en&as_sdt=0,31 | 6 | 2,018 |
Variational Memory Encoder-Decoder | 37 | neurips | 5 | 0 | 2023-06-15 17:55:24.845000 | https://github.com/thaihungle/VMED | 18 | Variational memory encoder-decoder | https://scholar.google.com/scholar?cluster=16470131384989674730&hl=en&as_sdt=0,10 | 4 | 2,018 |
Data-Efficient Hierarchical Reinforcement Learning | 690 | neurips | 46,276 | 1,206 | 2023-06-15 17:55:25.036000 | https://github.com/tensorflow/models | 75,922 | Data-efficient hierarchical reinforcement learning | https://scholar.google.com/scholar?cluster=8228365515476642671&hl=en&as_sdt=0,11 | 2,774 | 2,018 |
Removing the Feature Correlation Effect of Multiplicative Noise | 8 | neurips | 1 | 0 | 2023-06-15 17:55:25.226000 | https://github.com/zj10/NCMN | 3 | Removing the feature correlation effect of multiplicative noise | https://scholar.google.com/scholar?cluster=17402472050771179089&hl=en&as_sdt=0,5 | 1 | 2,018 |
Efficient Loss-Based Decoding on Graphs for Extreme Classification | 12 | neurips | 4 | 0 | 2023-06-15 17:55:25.417000 | https://github.com/ievron/wltls | 4 | Efficient loss-based decoding on graphs for extreme classification | https://scholar.google.com/scholar?cluster=17119928599826946784&hl=en&as_sdt=0,41 | 2 | 2,018 |
Scalable methods for 8-bit training of neural networks | 284 | neurips | 56 | 10 | 2023-06-15 17:55:25.607000 | https://github.com/eladhoffer/quantized.pytorch | 210 | Scalable methods for 8-bit training of neural networks | https://scholar.google.com/scholar?cluster=6261172322646700444&hl=en&as_sdt=0,10 | 13 | 2,018 |
Step Size Matters in Deep Learning | 26 | neurips | 1 | 0 | 2023-06-15 17:55:25.798000 | https://github.com/nar-k/NIPS-2018 | 3 | Step size matters in deep learning | https://scholar.google.com/scholar?cluster=5460214845816514152&hl=en&as_sdt=0,47 | 1 | 2,018 |
Dirichlet belief networks for topic structure learning | 29 | neurips | 4 | 2 | 2023-06-15 17:55:25.989000 | https://github.com/ethanhezhao/DirBN | 7 | Dirichlet belief networks for topic structure learning | https://scholar.google.com/scholar?cluster=13908644537239897303&hl=en&as_sdt=0,47 | 2 | 2,018 |
HOUDINI: Lifelong Learning as Program Synthesis | 68 | neurips | 5 | 0 | 2023-06-15 17:55:26.180000 | https://github.com/capergroup/houdini | 45 | Houdini: Lifelong learning as program synthesis | https://scholar.google.com/scholar?cluster=10841457222027435818&hl=en&as_sdt=0,33 | 6 | 2,018 |
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks | 39 | neurips | 4 | 1 | 2023-06-15 17:55:26.371000 | https://github.com/flatironinstitute/mantis | 10 | Manifold-tiling localized receptive fields are optimal in similarity-preserving neural networks | https://scholar.google.com/scholar?cluster=1758414387739465296&hl=en&as_sdt=0,47 | 3 | 2,018 |
Embedding Logical Queries on Knowledge Graphs | 228 | neurips | 39 | 9 | 2023-06-15 17:55:26.562000 | https://github.com/williamleif/graphqembed | 116 | Embedding logical queries on knowledge graphs | https://scholar.google.com/scholar?cluster=9948805019620970484&hl=en&as_sdt=0,5 | 8 | 2,018 |
Parsimonious Bayesian deep networks | 7 | neurips | 2 | 0 | 2023-06-15 17:55:26.752000 | https://github.com/mingyuanzhou/PBDN | 3 | Parsimonious Bayesian deep networks | https://scholar.google.com/scholar?cluster=14376157659087127451&hl=en&as_sdt=0,5 | 5 | 2,018 |
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion | 289 | neurips | 46,276 | 1,206 | 2023-06-15 17:55:26.943000 | https://github.com/tensorflow/models | 75,922 | Sample-efficient reinforcement learning with stochastic ensemble value expansion | https://scholar.google.com/scholar?cluster=12106658410656872341&hl=en&as_sdt=0,5 | 2,774 | 2,018 |
Neural Nearest Neighbors Networks | 292 | neurips | 44 | 17 | 2023-06-15 17:55:27.134000 | https://github.com/visinf/n3net | 276 | Neural nearest neighbors networks | https://scholar.google.com/scholar?cluster=11963067599142958734&hl=en&as_sdt=0,10 | 15 | 2,018 |
Neural Architecture Search with Bayesian Optimisation and Optimal Transport | 546 | neurips | 27 | 5 | 2023-06-15 17:55:27.325000 | https://github.com/kirthevasank/nasbot | 128 | Neural architecture search with bayesian optimisation and optimal transport | https://scholar.google.com/scholar?cluster=7308576573219301832&hl=en&as_sdt=0,11 | 12 | 2,018 |
BinGAN: Learning Compact Binary Descriptors with a Regularized GAN | 68 | neurips | 10 | 0 | 2023-06-15 17:55:27.526000 | https://github.com/maciejzieba/binGAN | 36 | Bingan: Learning compact binary descriptors with a regularized gan | https://scholar.google.com/scholar?cluster=7540991992898429437&hl=en&as_sdt=0,23 | 7 | 2,018 |
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing | 124 | neurips | 71 | 4 | 2023-06-15 17:55:27.717000 | https://github.com/crazydonkey200/neural-symbolic-machines | 371 | Memory augmented policy optimization for program synthesis and semantic parsing | https://scholar.google.com/scholar?cluster=4398387474099067788&hl=en&as_sdt=0,5 | 26 | 2,018 |
LF-Net: Learning Local Features from Images | 445 | neurips | 67 | 13 | 2023-06-15 17:55:27.908000 | https://github.com/vcg-uvic/lf-net-release | 300 | LF-Net: Learning local features from images | https://scholar.google.com/scholar?cluster=8243342192916977654&hl=en&as_sdt=0,5 | 19 | 2,018 |
PointCNN: Convolution On X-Transformed Points | 2,077 | neurips | 359 | 59 | 2023-06-15 17:55:28.099000 | https://github.com/yangyanli/PointCNN | 1,305 | Pointcnn: Convolution on x-transformed points | https://scholar.google.com/scholar?cluster=9461711858418183791&hl=en&as_sdt=0,47 | 56 | 2,018 |
Assessing Generative Models via Precision and Recall | 373 | neurips | 10 | 5 | 2023-06-15 17:55:28.289000 | https://github.com/msmsajjadi/precision-recall-distributions | 89 | Assessing generative models via precision and recall | https://scholar.google.com/scholar?cluster=651893942780229&hl=en&as_sdt=0,3 | 2 | 2,018 |
Improved Network Robustness with Adversary Critic | 13 | neurips | 0 | 0 | 2023-06-15 17:55:28.479000 | https://github.com/aam-at/adversary_critic | 13 | Improved network robustness with adversary critic | https://scholar.google.com/scholar?cluster=4193325299886417643&hl=en&as_sdt=0,47 | 4 | 2,018 |
Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators | 25 | neurips | 1 | 0 | 2023-06-15 17:55:28.670000 | https://github.com/keisuke198619/metricNLDS | 1 | Metric on nonlinear dynamical systems with perron-frobenius operators | https://scholar.google.com/scholar?cluster=9736849801126744369&hl=en&as_sdt=0,24 | 2 | 2,018 |
Non-Local Recurrent Network for Image Restoration | 536 | neurips | 39 | 0 | 2023-06-15 17:55:28.861000 | https://github.com/Ding-Liu/NLRN | 169 | Non-local recurrent network for image restoration | https://scholar.google.com/scholar?cluster=17713021931965385894&hl=en&as_sdt=0,11 | 14 | 2,018 |
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning | 11 | neurips | 1 | 1 | 2023-06-15 17:55:29.051000 | https://github.com/hsvgbkhgbv/TACTHMC | 7 | Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning | https://scholar.google.com/scholar?cluster=1359920802371030920&hl=en&as_sdt=0,22 | 3 | 2,018 |
A Stein variational Newton method | 114 | neurips | 3 | 0 | 2023-06-15 17:55:29.242000 | https://github.com/gianlucadetommaso/Stein-variational-samplers | 21 | A Stein variational Newton method | https://scholar.google.com/scholar?cluster=2381223671647654052&hl=en&as_sdt=0,5 | 4 | 2,018 |
Compositional Plan Vectors | 12 | neurips | 0 | 14 | 2023-06-15 23:42:32.928000 | https://github.com/cdevin/cpv | 8 | Compositional plan vectors | https://scholar.google.com/scholar?cluster=15635463865993301870&hl=en&as_sdt=0,5 | 4 | 2,019 |
Learning to Propagate for Graph Meta-Learning | 90 | neurips | 3 | 2 | 2023-06-15 23:42:33.114000 | https://github.com/liulu112601/Gated-Propagation-Net | 36 | Learning to propagate for graph meta-learning | https://scholar.google.com/scholar?cluster=3473165000863905721&hl=en&as_sdt=0,5 | 2 | 2,019 |
Multi-resolution Multi-task Gaussian Processes | 33 | neurips | 3 | 0 | 2023-06-15 23:42:33.297000 | https://github.com/ohamelijnck/multi_res_gps | 6 | Multi-resolution multi-task Gaussian processes | https://scholar.google.com/scholar?cluster=5029064741200470600&hl=en&as_sdt=0,26 | 1 | 2,019 |
Deep Equilibrium Models | 452 | neurips | 75 | 5 | 2023-06-15 23:42:33.479000 | https://github.com/locuslab/deq | 650 | Deep equilibrium models | https://scholar.google.com/scholar?cluster=659851965041196662&hl=en&as_sdt=0,5 | 20 | 2,019 |
Exact Gaussian Processes on a Million Data Points | 205 | neurips | 501 | 318 | 2023-06-15 23:42:33.662000 | https://github.com/cornellius-gp/gpytorch | 3,140 | Exact Gaussian processes on a million data points | https://scholar.google.com/scholar?cluster=4013716764327710087&hl=en&as_sdt=0,29 | 55 | 2,019 |
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization | 14 | neurips | 1 | 2 | 2023-06-15 23:42:33.844000 | https://github.com/sorooshafiee/Optimistic_Likelihoods | 3 | Calculating optimistic likelihoods using (geodesically) convex optimization | https://scholar.google.com/scholar?cluster=5806305643748445691&hl=en&as_sdt=0,14 | 1 | 2,019 |
Improved Precision and Recall Metric for Assessing Generative Models | 355 | neurips | 15 | 0 | 2023-06-15 23:42:34.026000 | https://github.com/kynkaat/improved-precision-and-recall-metric | 126 | Improved precision and recall metric for assessing generative models | https://scholar.google.com/scholar?cluster=16244569923752023320&hl=en&as_sdt=0,33 | 4 | 2,019 |
Zero-Shot Semantic Segmentation | 166 | neurips | 23 | 6 | 2023-06-15 23:42:34.208000 | https://github.com/valeoai/ZS3 | 170 | Zero-shot semantic segmentation | https://scholar.google.com/scholar?cluster=9122033339368914969&hl=en&as_sdt=0,49 | 14 | 2,019 |
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