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Predictive Information Accelerates Learning in RL
| 64 |
neurips
| 10 | 1 |
2023-06-16 15:11:11.889000
|
https://github.com/google-research/pisac
| 39 |
Predictive information accelerates learning in rl
|
https://scholar.google.com/scholar?cluster=10907320326175710661&hl=en&as_sdt=0,10
| 8 | 2,020 |
Counterexample-Guided Learning of Monotonic Neural Networks
| 38 |
neurips
| 7 | 2 |
2023-06-16 15:11:12.081000
|
https://github.com/AishwaryaSivaraman/COMET
| 16 |
Counterexample-guided learning of monotonic neural networks
|
https://scholar.google.com/scholar?cluster=5391837593184408852&hl=en&as_sdt=0,5
| 5 | 2,020 |
On the Trade-off between Adversarial and Backdoor Robustness
| 34 |
neurips
| 4 | 0 |
2023-06-16 15:11:12.282000
|
https://github.com/nthu-datalab/On.the.Trade-off.between.Adversarial.and.Backdoor.Robustness
| 16 |
On the trade-off between adversarial and backdoor robustness
|
https://scholar.google.com/scholar?cluster=10900350868300129860&hl=en&as_sdt=0,5
| 4 | 2,020 |
Implicit Graph Neural Networks
| 90 |
neurips
| 10 | 1 |
2023-06-16 15:11:12.496000
|
https://github.com/SwiftieH/IGNN
| 49 |
Implicit graph neural networks
|
https://scholar.google.com/scholar?cluster=18159437078590406343&hl=en&as_sdt=0,33
| 2 | 2,020 |
Rethinking Importance Weighting for Deep Learning under Distribution Shift
| 65 |
neurips
| 7 | 0 |
2023-06-16 15:11:12.689000
|
https://github.com/TongtongFANG/DIW
| 17 |
Rethinking importance weighting for deep learning under distribution shift
|
https://scholar.google.com/scholar?cluster=14240629165004038270&hl=en&as_sdt=0,33
| 1 | 2,020 |
Guiding Deep Molecular Optimization with Genetic Exploration
| 44 |
neurips
| 12 | 0 |
2023-06-16 15:11:12.881000
|
https://github.com/sungsoo-ahn/genetic-expert-guided-learning
| 19 |
Guiding deep molecular optimization with genetic exploration
|
https://scholar.google.com/scholar?cluster=14089467275472583248&hl=en&as_sdt=0,5
| 2 | 2,020 |
Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks
| 134 |
neurips
| 23 | 8 |
2023-06-16 15:11:13.075000
|
https://github.com/stonezwr/TSSL-BP
| 55 |
Temporal spike sequence learning via backpropagation for deep spiking neural networks
|
https://scholar.google.com/scholar?cluster=16845893280072286634&hl=en&as_sdt=0,10
| 2 | 2,020 |
TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation
| 69 |
neurips
| 15 | 3 |
2023-06-16 15:11:13.268000
|
https://github.com/verashira/TSPNet
| 97 |
Tspnet: Hierarchical feature learning via temporal semantic pyramid for sign language translation
|
https://scholar.google.com/scholar?cluster=16139838619918263139&hl=en&as_sdt=0,34
| 7 | 2,020 |
MetaPoison: Practical General-purpose Clean-label Data Poisoning
| 117 |
neurips
| 7 | 0 |
2023-06-16 15:11:13.492000
|
https://github.com/wronnyhuang/metapoison
| 40 |
Metapoison: Practical general-purpose clean-label data poisoning
|
https://scholar.google.com/scholar?cluster=12626791803327337128&hl=en&as_sdt=0,36
| 5 | 2,020 |
Training Generative Adversarial Networks with Limited Data
| 1,112 |
neurips
| 498 | 73 |
2023-06-16 15:11:13.686000
|
https://github.com/NVlabs/stylegan2-ada
| 1,731 |
Training generative adversarial networks with limited data
|
https://scholar.google.com/scholar?cluster=9063880872255850171&hl=en&as_sdt=0,1
| 37 | 2,020 |
Deeply Learned Spectral Total Variation Decomposition
| 5 |
neurips
| 1 | 0 |
2023-06-16 15:11:13.878000
|
https://github.com/TamaraGrossmann/TVspecNET
| 3 |
Deeply learned spectral total variation decomposition
|
https://scholar.google.com/scholar?cluster=7349648081709070834&hl=en&as_sdt=0,36
| 1 | 2,020 |
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training
| 27 |
neurips
| 4 | 1 |
2023-06-16 15:11:14.071000
|
https://github.com/RICE-EIC/FracTrain
| 11 |
Fractrain: Fractionally squeezing bit savings both temporally and spatially for efficient dnn training
|
https://scholar.google.com/scholar?cluster=1131091866886503352&hl=en&as_sdt=0,44
| 2 | 2,020 |
Improving Neural Network Training in Low Dimensional Random Bases
| 11 |
neurips
| 1 | 0 |
2023-06-16 15:11:14.264000
|
https://github.com/graphcore-research/random-bases
| 13 |
Improving neural network training in low dimensional random bases
|
https://scholar.google.com/scholar?cluster=13165492743270008587&hl=en&as_sdt=0,5
| 3 | 2,020 |
Safe Reinforcement Learning via Curriculum Induction
| 68 |
neurips
| 8 | 1 |
2023-06-16 15:11:14.457000
|
https://github.com/zuzuba/CISR_NeurIPS20
| 19 |
Safe reinforcement learning via curriculum induction
|
https://scholar.google.com/scholar?cluster=8445182531560403381&hl=en&as_sdt=0,47
| 2 | 2,020 |
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks
| 159 |
neurips
| 8 | 3 |
2023-06-16 15:11:14.649000
|
https://github.com/vunhatminh/PGMExplainer
| 54 |
Pgm-explainer: Probabilistic graphical model explanations for graph neural networks
|
https://scholar.google.com/scholar?cluster=2699838992970724085&hl=en&as_sdt=0,4
| 2 | 2,020 |
Few-Cost Salient Object Detection with Adversarial-Paced Learning
| 56 |
neurips
| 1 | 4 |
2023-06-16 15:11:14.841000
|
https://github.com/hb-stone/FC-SOD
| 16 |
Few-cost salient object detection with adversarial-paced learning
|
https://scholar.google.com/scholar?cluster=18093471542867628559&hl=en&as_sdt=0,10
| 2 | 2,020 |
Learning Black-Box Attackers with Transferable Priors and Query Feedback
| 45 |
neurips
| 4 | 1 |
2023-06-16 15:11:15.035000
|
https://github.com/TrustworthyDL/LeBA
| 28 |
Learning black-box attackers with transferable priors and query feedback
|
https://scholar.google.com/scholar?cluster=6702320856145728902&hl=en&as_sdt=0,43
| 3 | 2,020 |
Locally Differentially Private (Contextual) Bandits Learning
| 36 |
neurips
| 0 | 0 |
2023-06-16 15:11:15.227000
|
https://github.com/huang-research-group/LDPbandit2020
| 4 |
Locally differentially private (contextual) bandits learning
|
https://scholar.google.com/scholar?cluster=7254373858969503567&hl=en&as_sdt=0,5
| 1 | 2,020 |
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax
| 16 |
neurips
| 2 | 1 |
2023-06-16 15:11:15.421000
|
https://github.com/cunningham-lab/igr
| 24 |
Invertible gaussian reparameterization: Revisiting the gumbel-softmax
|
https://scholar.google.com/scholar?cluster=4895882618721897785&hl=en&as_sdt=0,33
| 5 | 2,020 |
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
| 20 |
neurips
| 2 | 0 |
2023-06-16 15:11:15.614000
|
https://github.com/ubisoft/ubisoft-la-forge-ASAF
| 14 |
Adversarial soft advantage fitting: Imitation learning without policy optimization
|
https://scholar.google.com/scholar?cluster=15547174239533139584&hl=en&as_sdt=0,37
| 5 | 2,020 |
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space
| 43 |
neurips
| 2 | 1 |
2023-06-16 15:11:15.806000
|
https://github.com/AnTuo1998/AE-KD
| 21 |
Agree to disagree: Adaptive ensemble knowledge distillation in gradient space
|
https://scholar.google.com/scholar?cluster=18027461890187573806&hl=en&as_sdt=0,5
| 2 | 2,020 |
Matérn Gaussian Processes on Riemannian Manifolds
| 71 |
neurips
| 7 | 0 |
2023-06-16 15:11:15.999000
|
https://github.com/spbu-math-cs/Riemannian-Gaussian-Processes
| 22 |
Matérn Gaussian processes on Riemannian manifolds
|
https://scholar.google.com/scholar?cluster=6279045067331501246&hl=en&as_sdt=0,11
| 7 | 2,020 |
Improved Techniques for Training Score-Based Generative Models
| 385 |
neurips
| 47 | 4 |
2023-06-16 15:11:16.193000
|
https://github.com/ermongroup/ncsnv2
| 201 |
Improved techniques for training score-based generative models
|
https://scholar.google.com/scholar?cluster=12852382198544252304&hl=en&as_sdt=0,36
| 14 | 2,020 |
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
| 2,397 |
neurips
| 5,869 | 1,030 |
2023-06-16 15:11:16.385000
|
https://github.com/pytorch/fairseq
| 26,463 |
wav2vec 2.0: A framework for self-supervised learning of speech representations
|
https://scholar.google.com/scholar?cluster=17012233978100358310&hl=en&as_sdt=0,5
| 411 | 2,020 |
Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients
| 36 |
neurips
| 70 | 118 |
2023-06-16 15:11:16.577000
|
https://github.com/wsmoses/Enzyme
| 971 |
Instead of rewriting foreign code for machine learning, automatically synthesize fast gradients
|
https://scholar.google.com/scholar?cluster=8551089294709765522&hl=en&as_sdt=0,47
| 37 | 2,020 |
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?
| 64 |
neurips
| 9 | 0 |
2023-06-16 15:11:16.770000
|
https://github.com/MSU-MLSys-Lab/arch2vec
| 44 |
Does unsupervised architecture representation learning help neural architecture search?
|
https://scholar.google.com/scholar?cluster=1242457712275613976&hl=en&as_sdt=0,32
| 5 | 2,020 |
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
| 52 |
neurips
| 0 | 0 |
2023-06-16 15:11:16.962000
|
https://github.com/theryanl/mitigating_manipulation_via_randomized_reviewer_assignment
| 1 |
Mitigating manipulation in peer review via randomized reviewer assignments
|
https://scholar.google.com/scholar?cluster=8604310710998077908&hl=en&as_sdt=0,39
| 1 | 2,020 |
Contrastive learning of global and local features for medical image segmentation with limited annotations
| 307 |
neurips
| 39 | 4 |
2023-06-16 15:11:17.155000
|
https://github.com/krishnabits001/domain_specific_cl
| 163 |
Contrastive learning of global and local features for medical image segmentation with limited annotations
|
https://scholar.google.com/scholar?cluster=4824494533053964264&hl=en&as_sdt=0,23
| 2 | 2,020 |
Self-Supervised Graph Transformer on Large-Scale Molecular Data
| 330 |
neurips
| 56 | 13 |
2023-06-16 15:11:17.347000
|
https://github.com/tencent-ailab/grover
| 257 |
Self-supervised graph transformer on large-scale molecular data
|
https://scholar.google.com/scholar?cluster=697764344389876578&hl=en&as_sdt=0,33
| 4 | 2,020 |
Generative Neurosymbolic Machines
| 45 |
neurips
| 4 | 0 |
2023-06-16 15:11:17.541000
|
https://github.com/JindongJiang/GNM
| 30 |
Generative neurosymbolic machines
|
https://scholar.google.com/scholar?cluster=8665652977960746383&hl=en&as_sdt=0,5
| 3 | 2,020 |
Efficient estimation of neural tuning during naturalistic behavior
| 9 |
neurips
| 1 | 0 |
2023-06-16 15:11:17.734000
|
https://github.com/BalzaniEdoardo/PGAM
| 1 |
Efficient estimation of neural tuning during naturalistic behavior
|
https://scholar.google.com/scholar?cluster=3674318133407421247&hl=en&as_sdt=0,5
| 4 | 2,020 |
High-recall causal discovery for autocorrelated time series with latent confounders
| 45 |
neurips
| 224 | 7 |
2023-06-16 15:11:17.927000
|
https://github.com/jakobrunge/tigramite
| 925 |
High-recall causal discovery for autocorrelated time series with latent confounders
|
https://scholar.google.com/scholar?cluster=6795430234253215305&hl=en&as_sdt=0,46
| 37 | 2,020 |
Joint Contrastive Learning with Infinite Possibilities
| 46 |
neurips
| 7 | 0 |
2023-06-16 15:11:18.120000
|
https://github.com/caiqi/Joint-Contrastive-Learning
| 41 |
Joint contrastive learning with infinite possibilities
|
https://scholar.google.com/scholar?cluster=6409005295330808572&hl=en&as_sdt=0,11
| 1 | 2,020 |
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
| 67 |
neurips
| 34 | 9 |
2023-06-16 15:11:18.315000
|
https://github.com/didriknielsen/survae_flows
| 276 |
Survae flows: Surjections to bridge the gap between vaes and flows
|
https://scholar.google.com/scholar?cluster=1881827871992475792&hl=en&as_sdt=0,5
| 28 | 2,020 |
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes
| 2 |
neurips
| 0 | 0 |
2023-06-16 15:11:18.526000
|
https://github.com/hqminh/gp_sketch_nips
| 1 |
Revisiting the sample complexity of sparse spectrum approximation of gaussian processes
|
https://scholar.google.com/scholar?cluster=8244493661366176703&hl=en&as_sdt=0,5
| 2 | 2,020 |
Incorporating Interpretable Output Constraints in Bayesian Neural Networks
| 9 |
neurips
| 6 | 2 |
2023-06-16 15:11:18.721000
|
https://github.com/dtak/ocbnn-public
| 37 |
Incorporating interpretable output constraints in Bayesian neural networks
|
https://scholar.google.com/scholar?cluster=18422416496972996615&hl=en&as_sdt=0,5
| 27 | 2,020 |
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
| 64 |
neurips
| 10 | 0 |
2023-06-16 15:11:18.922000
|
https://github.com/biomedia-mira/stochastic_segmentation_networks
| 58 |
Stochastic segmentation networks: Modelling spatially correlated aleatoric uncertainty
|
https://scholar.google.com/scholar?cluster=2760463474925616365&hl=en&as_sdt=0,15
| 5 | 2,020 |
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA
| 45 |
neurips
| 14 | 5 |
2023-06-16 15:11:19.114000
|
https://github.com/ilkhem/icebeem
| 69 |
Ice-beem: Identifiable conditional energy-based deep models based on nonlinear ica
|
https://scholar.google.com/scholar?cluster=384384070295711356&hl=en&as_sdt=0,5
| 2 | 2,020 |
CogLTX: Applying BERT to Long Texts
| 90 |
neurips
| 45 | 15 |
2023-06-16 15:11:19.307000
|
https://github.com/Sleepychord/CogLTX
| 237 |
Cogltx: Applying bert to long texts
|
https://scholar.google.com/scholar?cluster=18138927852402221262&hl=en&as_sdt=0,5
| 3 | 2,020 |
Uncertainty Aware Semi-Supervised Learning on Graph Data
| 51 |
neurips
| 7 | 0 |
2023-06-16 15:11:19.500000
|
https://github.com/zxj32/uncertainty-GNN
| 31 |
Uncertainty aware semi-supervised learning on graph data
|
https://scholar.google.com/scholar?cluster=4897163804428494443&hl=en&as_sdt=0,5
| 1 | 2,020 |
ConvBERT: Improving BERT with Span-based Dynamic Convolution
| 118 |
neurips
| 52 | 5 |
2023-06-16 15:11:19.692000
|
https://github.com/yitu-opensource/ConvBert
| 239 |
Convbert: Improving bert with span-based dynamic convolution
|
https://scholar.google.com/scholar?cluster=10192234385431493258&hl=en&as_sdt=0,41
| 9 | 2,020 |
Practical No-box Adversarial Attacks against DNNs
| 34 |
neurips
| 3 | 1 |
2023-06-16 15:11:19.886000
|
https://github.com/qizhangli/nobox-attacks
| 28 |
Practical no-box adversarial attacks against dnns
|
https://scholar.google.com/scholar?cluster=6838267970372396918&hl=en&as_sdt=0,5
| 2 | 2,020 |
Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization
| 6 |
neurips
| 1 | 0 |
2023-06-16 15:11:20.078000
|
https://github.com/hassanmortagy/Walking-in-the-Shadow
| 1 |
Walking in the shadow: A new perspective on descent directions for constrained minimization
|
https://scholar.google.com/scholar?cluster=1091839893594685655&hl=en&as_sdt=0,33
| 2 | 2,020 |
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation
| 59 |
neurips
| 8 | 3 |
2023-06-16 15:11:20.271000
|
https://github.com/yogeshbalaji/robustOT
| 37 |
Robust optimal transport with applications in generative modeling and domain adaptation
|
https://scholar.google.com/scholar?cluster=12381846774517697347&hl=en&as_sdt=0,21
| 1 | 2,020 |
Autofocused oracles for model-based design
| 48 |
neurips
| 0 | 1 |
2023-06-16 15:11:20.479000
|
https://github.com/clarafy/autofocused_oracles
| 7 |
Autofocused oracles for model-based design
|
https://scholar.google.com/scholar?cluster=6937579487208451262&hl=en&as_sdt=0,3
| 1 | 2,020 |
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
| 22 |
neurips
| 5 | 0 |
2023-06-16 15:11:20.671000
|
https://github.com/younggyoseo/trajectory_mcl
| 36 |
Trajectory-wise multiple choice learning for dynamics generalization in reinforcement learning
|
https://scholar.google.com/scholar?cluster=648830007414407622&hl=en&as_sdt=0,33
| 3 | 2,020 |
CompRess: Self-Supervised Learning by Compressing Representations
| 56 |
neurips
| 12 | 0 |
2023-06-16 15:11:20.863000
|
https://github.com/UMBCvision/CompReSS
| 73 |
Compress: Self-supervised learning by compressing representations
|
https://scholar.google.com/scholar?cluster=6444771032611059422&hl=en&as_sdt=0,5
| 5 | 2,020 |
Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN
| 16 |
neurips
| 3 | 1 |
2023-06-16 15:11:21.055000
|
https://github.com/duolala1/Reconstructing-Perceptive-Images-from-Brain-Activity-by-Shape-Semantic-GAN
| 13 |
Reconstructing perceptive images from brain activity by shape-semantic GAN
|
https://scholar.google.com/scholar?cluster=18044755082798532975&hl=en&as_sdt=0,10
| 2 | 2,020 |
A Spectral Energy Distance for Parallel Speech Synthesis
| 44 |
neurips
| 7,320 | 1,025 |
2023-06-16 15:11:21.248000
|
https://github.com/google-research/google-research
| 29,776 |
A spectral energy distance for parallel speech synthesis
|
https://scholar.google.com/scholar?cluster=9787276349444445830&hl=en&as_sdt=0,5
| 727 | 2,020 |
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations
| 125 |
neurips
| 41 | 5 |
2023-06-16 15:11:21.441000
|
https://github.com/dicarlolab/vonenet
| 103 |
Simulating a primary visual cortex at the front of CNNs improves robustness to image perturbations
|
https://scholar.google.com/scholar?cluster=14266709854899740173&hl=en&as_sdt=0,44
| 15 | 2,020 |
Learning from Positive and Unlabeled Data with Arbitrary Positive Shift
| 13 |
neurips
| 3 | 2 |
2023-06-16 15:11:21.633000
|
https://github.com/ZaydH/arbitrary_pu
| 12 |
Learning from positive and unlabeled data with arbitrary positive shift
|
https://scholar.google.com/scholar?cluster=15991809969582276499&hl=en&as_sdt=0,34
| 4 | 2,020 |
Deep Energy-based Modeling of Discrete-Time Physics
| 38 |
neurips
| 0 | 0 |
2023-06-16 15:11:21.826000
|
https://github.com/tksmatsubara/discrete-autograd
| 14 |
Deep energy-based modeling of discrete-time physics
|
https://scholar.google.com/scholar?cluster=17442296376869037659&hl=en&as_sdt=0,31
| 2 | 2,020 |
Self-Learning Transformations for Improving Gaze and Head Redirection
| 31 |
neurips
| 13 | 5 |
2023-06-16 15:11:22.019000
|
https://github.com/swook/faze_preprocess
| 36 |
Self-learning transformations for improving gaze and head redirection
|
https://scholar.google.com/scholar?cluster=5970866983104779512&hl=en&as_sdt=0,22
| 3 | 2,020 |
Language-Conditioned Imitation Learning for Robot Manipulation Tasks
| 73 |
neurips
| 18 | 4 |
2023-06-16 15:11:22.212000
|
https://github.com/ir-lab/LanguagePolicies
| 56 |
Language-conditioned imitation learning for robot manipulation tasks
|
https://scholar.google.com/scholar?cluster=6592795961085192473&hl=en&as_sdt=0,26
| 4 | 2,020 |
Node Embeddings and Exact Low-Rank Representations of Complex Networks
| 25 |
neurips
| 2 | 0 |
2023-06-16 15:11:22.405000
|
https://github.com/schariya/exact-embeddings
| 1 |
Node embeddings and exact low-rank representations of complex networks
|
https://scholar.google.com/scholar?cluster=7942197443873251549&hl=en&as_sdt=0,33
| 3 | 2,020 |
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications
| 66 |
neurips
| 820 | 36 |
2023-06-16 15:11:22.597000
|
https://github.com/deepmind/open_spiel
| 3,697 |
Fictitious play for mean field games: Continuous time analysis and applications
|
https://scholar.google.com/scholar?cluster=15909658431053288076&hl=en&as_sdt=0,6
| 106 | 2,020 |
Interferobot: aligning an optical interferometer by a reinforcement learning agent
| 11 |
neurips
| 0 | 1 |
2023-06-16 15:11:22.790000
|
https://github.com/dmitrySorokin/interferobotProject
| 8 |
Interferobot: aligning an optical interferometer by a reinforcement learning agent
|
https://scholar.google.com/scholar?cluster=2017472169343079097&hl=en&as_sdt=0,33
| 2 | 2,020 |
Principal Neighbourhood Aggregation for Graph Nets
| 375 |
neurips
| 54 | 1 |
2023-06-16 15:11:22.982000
|
https://github.com/lukecavabarrett/pna
| 309 |
Principal neighbourhood aggregation for graph nets
|
https://scholar.google.com/scholar?cluster=16853110833313152641&hl=en&as_sdt=0,33
| 5 | 2,020 |
Instance Selection for GANs
| 34 |
neurips
| 4 | 3 |
2023-06-16 15:11:23.175000
|
https://github.com/uoguelph-mlrg/instance_selection_for_gans
| 42 |
Instance selection for gans
|
https://scholar.google.com/scholar?cluster=17012682042599095713&hl=en&as_sdt=0,44
| 7 | 2,020 |
Video Frame Interpolation without Temporal Priors
| 18 |
neurips
| 3 | 1 |
2023-06-16 15:11:23.367000
|
https://github.com/yjzhang96/UTI-VFI
| 31 |
Video frame interpolation without temporal priors
|
https://scholar.google.com/scholar?cluster=2687678947317958892&hl=en&as_sdt=0,33
| 4 | 2,020 |
Learning compositional functions via multiplicative weight updates
| 14 |
neurips
| 0 | 0 |
2023-06-16 15:11:23.564000
|
https://github.com/jxbz/madam
| 48 |
Learning compositional functions via multiplicative weight updates
|
https://scholar.google.com/scholar?cluster=4109629922045417832&hl=en&as_sdt=0,5
| 6 | 2,020 |
The interplay between randomness and structure during learning in RNNs
| 34 |
neurips
| 4 | 0 |
2023-06-16 15:11:23.756000
|
https://github.com/frschu/neurips_2020_interplay_randomness_structure
| 2 |
The interplay between randomness and structure during learning in RNNs
|
https://scholar.google.com/scholar?cluster=12747185201874235106&hl=en&as_sdt=0,33
| 1 | 2,020 |
Group Contextual Encoding for 3D Point Clouds
| 4 |
neurips
| 2 | 0 |
2023-06-16 15:11:23.949000
|
https://github.com/AsahiLiu/PointDetectron
| 17 |
Group contextual encoding for 3d point clouds
|
https://scholar.google.com/scholar?cluster=18035326901258524486&hl=en&as_sdt=0,33
| 1 | 2,020 |
Is normalization indispensable for training deep neural network?
| 43 |
neurips
| 2 | 0 |
2023-06-16 15:11:24.141000
|
https://github.com/hukkai/rescaling
| 33 |
Is normalization indispensable for training deep neural network?
|
https://scholar.google.com/scholar?cluster=13638844365029775861&hl=en&as_sdt=0,34
| 1 | 2,020 |
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks
| 43 |
neurips
| 3 | 1 |
2023-06-16 15:11:24.333000
|
https://github.com/AmirooR/IntraOrderPreservingCalibration
| 11 |
Intra order-preserving functions for calibration of multi-class neural networks
|
https://scholar.google.com/scholar?cluster=4818750365991041990&hl=en&as_sdt=0,33
| 3 | 2,020 |
Linear Time Sinkhorn Divergences using Positive Features
| 16 |
neurips
| 4 | 0 |
2023-06-16 15:11:24.525000
|
https://github.com/meyerscetbon/LinearSinkhorn
| 16 |
Linear time Sinkhorn divergences using positive features
|
https://scholar.google.com/scholar?cluster=5122167736110613142&hl=en&as_sdt=0,43
| 2 | 2,020 |
VarGrad: A Low-Variance Gradient Estimator for Variational Inference
| 14 |
neurips
| 0 | 1 |
2023-06-16 15:11:24.717000
|
https://github.com/aboustati/vargrad
| 12 |
VarGrad: a low-variance gradient estimator for variational inference
|
https://scholar.google.com/scholar?cluster=16870506199120747314&hl=en&as_sdt=0,33
| 4 | 2,020 |
A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction
| 6 |
neurips
| 0 | 2 |
2023-06-16 15:11:24.909000
|
https://github.com/WuLoli/CAECseq
| 2 |
A convolutional auto-encoder for haplotype assembly and viral quasispecies reconstruction
|
https://scholar.google.com/scholar?cluster=627030552528297106&hl=en&as_sdt=0,33
| 1 | 2,020 |
Adversarial Counterfactual Learning and Evaluation for Recommender System
| 24 |
neurips
| 8 | 0 |
2023-06-16 15:11:25.102000
|
https://github.com/StatsDLMathsRecomSys/Adversarial-Counterfactual-Learning-and-Evaluation-for-Recommender-System
| 21 |
Adversarial counterfactual learning and evaluation for recommender system
|
https://scholar.google.com/scholar?cluster=8553459307205349621&hl=en&as_sdt=0,16
| 2 | 2,020 |
Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control
| 16 |
neurips
| 4 | 0 |
2023-06-16 15:11:25.295000
|
https://github.com/giorgosmamakoukas/MemoryEfficientStableLDS
| 17 |
Memory-efficient learning of stable linear dynamical systems for prediction and control
|
https://scholar.google.com/scholar?cluster=6270757032099202742&hl=en&as_sdt=0,33
| 2 | 2,020 |
RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder
| 53 |
neurips
| 26 | 10 |
2023-06-16 15:11:25.498000
|
https://github.com/microsoft/RelationNet2
| 209 |
Relationnet++: Bridging visual representations for object detection via transformer decoder
|
https://scholar.google.com/scholar?cluster=2597487558534489609&hl=en&as_sdt=0,33
| 24 | 2,020 |
Neurosymbolic Transformers for Multi-Agent Communication
| 18 |
neurips
| 2 | 0 |
2023-06-16 15:11:25.690000
|
https://github.com/jinala/multi-agent-neurosym-transformers
| 16 |
Neurosymbolic transformers for multi-agent communication
|
https://scholar.google.com/scholar?cluster=4554423143327303574&hl=en&as_sdt=0,33
| 1 | 2,020 |
Fairness in Streaming Submodular Maximization: Algorithms and Hardness
| 29 |
neurips
| 7,320 | 1,025 |
2023-06-16 15:11:25.882000
|
https://github.com/google-research/google-research
| 29,776 |
Fairness in streaming submodular maximization: Algorithms and hardness
|
https://scholar.google.com/scholar?cluster=762679963021898212&hl=en&as_sdt=0,11
| 727 | 2,020 |
Smoothed Geometry for Robust Attribution
| 36 |
neurips
| 1 | 0 |
2023-06-16 15:11:26.075000
|
https://github.com/zifanw/smoothed_geometry
| 7 |
Smoothed geometry for robust attribution
|
https://scholar.google.com/scholar?cluster=9573430737133381882&hl=en&as_sdt=0,5
| 1 | 2,020 |
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms
| 20 |
neurips
| 0 | 0 |
2023-06-16 15:11:26.266000
|
https://github.com/saralajew/robust_NPCs
| 2 |
Fast adversarial robustness certification of nearest prototype classifiers for arbitrary seminorms
|
https://scholar.google.com/scholar?cluster=5975974193120629268&hl=en&as_sdt=0,39
| 1 | 2,020 |
Multi-agent active perception with prediction rewards
| 9 |
neurips
| 0 | 0 |
2023-06-16 15:11:26.459000
|
https://github.com/laurimi/multiagent-prediction-reward
| 9 |
Multi-agent active perception with prediction rewards
|
https://scholar.google.com/scholar?cluster=16061069900902871568&hl=en&as_sdt=0,50
| 4 | 2,020 |
CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations
| 53 |
neurips
| 6 | 1 |
2023-06-16 15:11:26.650000
|
https://github.com/davrempe/caspr
| 66 |
Caspr: Learning canonical spatiotemporal point cloud representations
|
https://scholar.google.com/scholar?cluster=2193354072785076555&hl=en&as_sdt=0,18
| 10 | 2,020 |
Deep Automodulators
| 2 |
neurips
| 2 | 0 |
2023-06-16 15:11:26.842000
|
https://github.com/AaltoVision/automodulator
| 14 |
Deep automodulators
|
https://scholar.google.com/scholar?cluster=15567958931014667232&hl=en&as_sdt=0,33
| 2 | 2,020 |
Convolutional Tensor-Train LSTM for Spatio-Temporal Learning
| 83 |
neurips
| 31 | 15 |
2023-06-16 15:11:27.035000
|
https://github.com/NVlabs/conv-tt-lstm
| 111 |
Convolutional tensor-train lstm for spatio-temporal learning
|
https://scholar.google.com/scholar?cluster=14678206680499883821&hl=en&as_sdt=0,5
| 5 | 2,020 |
The Potts-Ising model for discrete multivariate data
| 3 |
neurips
| 0 | 0 |
2023-06-16 15:11:27.227000
|
https://github.com/aaamini/pois_comparisons
| 1 |
The Potts-Ising model for discrete multivariate data
|
https://scholar.google.com/scholar?cluster=6863880968599010032&hl=en&as_sdt=0,33
| 1 | 2,020 |
MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics
| 5 |
neurips
| 1 | 0 |
2023-06-16 15:11:27.419000
|
https://github.com/stephaneckstein/minmaxot
| 1 |
Minmax methods for optimal transport and beyond: Regularization, approximation and numerics
|
https://scholar.google.com/scholar?cluster=13129304767916620268&hl=en&as_sdt=0,33
| 1 | 2,020 |
A Discrete Variational Recurrent Topic Model without the Reparametrization Trick
| 22 |
neurips
| 2 | 1 |
2023-06-16 15:11:27.611000
|
https://github.com/mmrezaee/VRTM
| 10 |
A discrete variational recurrent topic model without the reparametrization trick
|
https://scholar.google.com/scholar?cluster=14340020828627005891&hl=en&as_sdt=0,33
| 2 | 2,020 |
Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces
| 8 |
neurips
| 0 | 0 |
2023-06-16 15:11:27.803000
|
https://github.com/akashsaha06/NeurIPS-2020
| 2 |
Learning with operator-valued kernels in reproducing kernel Krein spaces
|
https://scholar.google.com/scholar?cluster=13474809885434499902&hl=en&as_sdt=0,32
| 1 | 2,020 |
Learning Bounds for Risk-sensitive Learning
| 35 |
neurips
| 1 | 0 |
2023-06-16 15:11:27.996000
|
https://github.com/jaeho-lee/oce
| 5 |
Learning bounds for risk-sensitive learning
|
https://scholar.google.com/scholar?cluster=14340544354224111780&hl=en&as_sdt=0,13
| 1 | 2,020 |
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints
| 92 |
neurips
| 13 | 0 |
2023-06-16 15:11:28.189000
|
https://github.com/mfinzi/constrained-hamiltonian-neural-networks
| 87 |
Simplifying hamiltonian and lagrangian neural networks via explicit constraints
|
https://scholar.google.com/scholar?cluster=2817099507045066025&hl=en&as_sdt=0,15
| 5 | 2,020 |
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
| 60 |
neurips
| 1 | 1 |
2023-06-16 15:11:28.382000
|
https://github.com/wichmann-lab/error-consistency
| 6 |
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
|
https://scholar.google.com/scholar?cluster=13784841370093089337&hl=en&as_sdt=0,33
| 3 | 2,020 |
RANet: Region Attention Network for Semantic Segmentation
| 23 |
neurips
| 3 | 0 |
2023-06-16 15:11:28.575000
|
https://github.com/dingguo1996/RANet
| 32 |
Ranet: Region attention network for semantic segmentation
|
https://scholar.google.com/scholar?cluster=10094620109587185343&hl=en&as_sdt=0,41
| 3 | 2,020 |
Learning sparse codes from compressed representations with biologically plausible local wiring constraints
| 2 |
neurips
| 0 | 0 |
2023-06-16 15:11:28.768000
|
https://github.com/siplab-gt/localized-sparse-coding
| 1 |
Learning sparse codes from compressed representations with biologically plausible local wiring constraints
|
https://scholar.google.com/scholar?cluster=16665600177860294505&hl=en&as_sdt=0,10
| 2 | 2,020 |
Directional Pruning of Deep Neural Networks
| 26 |
neurips
| 8 | 2 |
2023-06-16 15:11:28.960000
|
https://github.com/donlan2710/gRDA-Optimizer
| 40 |
Directional pruning of deep neural networks
|
https://scholar.google.com/scholar?cluster=8389784571669099879&hl=en&as_sdt=0,5
| 3 | 2,020 |
NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity
| 10 |
neurips
| 4 | 0 |
2023-06-16 15:11:29.152000
|
https://github.com/L0SG/NanoFlow
| 64 |
Nanoflow: Scalable normalizing flows with sublinear parameter complexity
|
https://scholar.google.com/scholar?cluster=2139954886810739910&hl=en&as_sdt=0,5
| 4 | 2,020 |
Graph Cross Networks with Vertex Infomax Pooling
| 44 |
neurips
| 10 | 7 |
2023-06-16 15:11:29.345000
|
https://github.com/limaosen0/GXN
| 44 |
Graph cross networks with vertex infomax pooling
|
https://scholar.google.com/scholar?cluster=12147623399962209676&hl=en&as_sdt=0,5
| 4 | 2,020 |
MOPO: Model-based Offline Policy Optimization
| 454 |
neurips
| 40 | 9 |
2023-06-16 15:11:29.537000
|
https://github.com/tianheyu927/mopo
| 142 |
Mopo: Model-based offline policy optimization
|
https://scholar.google.com/scholar?cluster=17944635357002581259&hl=en&as_sdt=0,47
| 8 | 2,020 |
Building powerful and equivariant graph neural networks with structural message-passing
| 85 |
neurips
| 2 | 1 |
2023-06-16 15:11:29.729000
|
https://github.com/cvignac/SMP
| 21 |
Building powerful and equivariant graph neural networks with structural message-passing
|
https://scholar.google.com/scholar?cluster=9701369192475707124&hl=en&as_sdt=0,5
| 4 | 2,020 |
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
| 54 |
neurips
| 7 | 2 |
2023-06-16 15:11:29.922000
|
https://github.com/sebascuri/hucrl
| 28 |
Efficient model-based reinforcement learning through optimistic policy search and planning
|
https://scholar.google.com/scholar?cluster=13950651557612001480&hl=en&as_sdt=0,5
| 2 | 2,020 |
Practical Low-Rank Communication Compression in Decentralized Deep Learning
| 26 |
neurips
| 1 | 0 |
2023-06-16 15:11:30.115000
|
https://github.com/epfml/powergossip
| 7 |
Practical low-rank communication compression in decentralized deep learning
|
https://scholar.google.com/scholar?cluster=326168580277977318&hl=en&as_sdt=0,20
| 6 | 2,020 |
3D Shape Reconstruction from Vision and Touch
| 27 |
neurips
| 13 | 0 |
2023-06-16 15:11:30.307000
|
https://github.com/facebookresearch/3D-Vision-and-Touch
| 59 |
3d shape reconstruction from vision and touch
|
https://scholar.google.com/scholar?cluster=5012332327817595589&hl=en&as_sdt=0,39
| 10 | 2,020 |
GradAug: A New Regularization Method for Deep Neural Networks
| 22 |
neurips
| 6 | 1 |
2023-06-16 15:11:30.521000
|
https://github.com/taoyang1122/GradAug
| 90 |
Gradaug: A new regularization method for deep neural networks
|
https://scholar.google.com/scholar?cluster=6983882752578782153&hl=en&as_sdt=0,36
| 6 | 2,020 |
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay
| 36 |
neurips
| 5 | 0 |
2023-06-16 15:11:30.714000
|
https://github.com/sfujim/LAP-PAL
| 27 |
An equivalence between loss functions and non-uniform sampling in experience replay
|
https://scholar.google.com/scholar?cluster=7573921906024948700&hl=en&as_sdt=0,5
| 1 | 2,020 |
Rational neural networks
| 40 |
neurips
| 4 | 1 |
2023-06-16 15:11:30.906000
|
https://github.com/NBoulle/RationalNets
| 18 |
Rational neural networks
|
https://scholar.google.com/scholar?cluster=12116003355526084292&hl=en&as_sdt=0,5
| 5 | 2,020 |
DISK: Learning local features with policy gradient
| 124 |
neurips
| 31 | 2 |
2023-06-16 15:11:31.098000
|
https://github.com/cvlab-epfl/disk
| 217 |
DISK: Learning local features with policy gradient
|
https://scholar.google.com/scholar?cluster=3357995340662303301&hl=en&as_sdt=0,5
| 13 | 2,020 |
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