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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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