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Stochastic Backpropagation: A Memory Efficient Strategy for Training Video Models | 3 | cvpr | 3 | 0 | 2023-06-03 15:14:07.519000 | https://github.com/amazon-research/stochastic-backpropagation | 14 | Stochastic backpropagation: a memory efficient strategy for training video models | https://scholar.google.com/scholar?cluster=14270713915190496906&hl=en&as_sdt=0,33 | 7 | 2,022 |
Semantic-Shape Adaptive Feature Modulation for Semantic Image Synthesis | 8 | cvpr | 4 | 3 | 2023-06-03 15:14:07.715000 | https://github.com/cszy98/safm | 26 | Semantic-shape adaptive feature modulation for semantic image synthesis | https://scholar.google.com/scholar?cluster=17318944890457615747&hl=en&as_sdt=0,44 | 1 | 2,022 |
FIBA: Frequency-Injection Based Backdoor Attack in Medical Image Analysis | 17 | cvpr | 2 | 2 | 2023-06-03 15:14:07.910000 | https://github.com/hazardfy/fiba | 17 | Fiba: Frequency-injection based backdoor attack in medical image analysis | https://scholar.google.com/scholar?cluster=6976191591785103697&hl=en&as_sdt=0,33 | 2 | 2,022 |
Commonality in Natural Images Rescues GANs: Pretraining GANs With Generic and Privacy-Free Synthetic Data | 1 | cvpr | 0 | 0 | 2023-06-03 15:14:08.104000 | https://github.com/friedronaldo/primitives-ps | 33 | Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic Data | https://scholar.google.com/scholar?cluster=8826334023816517029&hl=en&as_sdt=0,33 | 1 | 2,022 |
Day-to-Night Image Synthesis for Training Nighttime Neural ISPs | 4 | cvpr | 3 | 1 | 2023-06-03 15:14:08.299000 | https://github.com/samsunglabs/day-to-night | 66 | Day-to-Night Image Synthesis for Training Nighttime Neural ISPs | https://scholar.google.com/scholar?cluster=10299773427115687036&hl=en&as_sdt=0,34 | 8 | 2,022 |
Deep Constrained Least Squares for Blind Image Super-Resolution | 21 | cvpr | 18 | 19 | 2023-06-03 15:14:08.493000 | https://github.com/megvii-research/dcls-sr | 170 | Deep constrained least squares for blind image super-resolution | https://scholar.google.com/scholar?cluster=11348834494517803103&hl=en&as_sdt=0,11 | 10 | 2,022 |
Beyond a Pre-Trained Object Detector: Cross-Modal Textual and Visual Context for Image Captioning | 17 | cvpr | 8 | 9 | 2023-06-03 15:14:08.688000 | https://github.com/GT-RIPL/Xmodal-Ctx | 51 | Beyond a pre-trained object detector: Cross-modal textual and visual context for image captioning | https://scholar.google.com/scholar?cluster=10614457451063447772&hl=en&as_sdt=0,5 | 2 | 2,022 |
From Representation to Reasoning: Towards Both Evidence and Commonsense Reasoning for Video Question-Answering | 10 | cvpr | 2 | 1 | 2023-06-03 15:14:08.883000 | https://github.com/bcmi/causal-vidqa | 34 | From Representation to Reasoning: Towards both Evidence and Commonsense Reasoning for Video Question-Answering | https://scholar.google.com/scholar?cluster=17266341443850491372&hl=en&as_sdt=0,10 | 8 | 2,022 |
DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion | 44 | cvpr | 27 | 11 | 2023-06-03 15:14:09.077000 | https://github.com/DanceTrack/DanceTrack | 297 | Dancetrack: Multi-object tracking in uniform appearance and diverse motion | https://scholar.google.com/scholar?cluster=9529319158101525799&hl=en&as_sdt=0,18 | 5 | 2,022 |
TubeDETR: Spatio-Temporal Video Grounding With Transformers | 30 | cvpr | 8 | 4 | 2023-06-03 15:14:09.272000 | https://github.com/antoyang/TubeDETR | 124 | Tubedetr: Spatio-temporal video grounding with transformers | https://scholar.google.com/scholar?cluster=10434862692373421904&hl=en&as_sdt=0,47 | 3 | 2,022 |
SLIC: Self-Supervised Learning With Iterative Clustering for Human Action Videos | 6 | cvpr | 1 | 4 | 2023-06-03 15:14:09.466000 | https://github.com/rvl-lab-utoronto/video_similarity_search | 15 | Slic: Self-supervised learning with iterative clustering for human action videos | https://scholar.google.com/scholar?cluster=17806290374737598520&hl=en&as_sdt=0,47 | 2 | 2,022 |
UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection | 25 | cvpr | 5 | 0 | 2023-06-03 15:14:09.660000 | https://github.com/lilygeorgescu/ubnormal | 47 | Ubnormal: New benchmark for supervised open-set video anomaly detection | https://scholar.google.com/scholar?cluster=8511572493070462818&hl=en&as_sdt=0,5 | 5 | 2,022 |
Beyond Cross-View Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image | 14 | cvpr | 8 | 1 | 2023-06-03 15:14:09.854000 | https://github.com/shiyujiao/highlyaccurate | 50 | Beyond cross-view image retrieval: Highly accurate vehicle localization using satellite image | https://scholar.google.com/scholar?cluster=3818502605593451777&hl=en&as_sdt=0,23 | 2 | 2,022 |
On GANs and GMMs | 136 | neurips | 19 | 1 | 2023-06-15 17:54:35.804000 | https://github.com/eitanrich/gans-n-gmms | 61 | On gans and gmms | https://scholar.google.com/scholar?cluster=809414118731916677&hl=en&as_sdt=0,44 | 3 | 2,018 |
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks | 198 | neurips | 15 | 3 | 2023-06-15 17:54:36.014000 | https://github.com/hyeonseob-nam/Batch-Instance-Normalization | 75 | Batch-instance normalization for adaptively style-invariant neural networks | https://scholar.google.com/scholar?cluster=10695085476541761892&hl=en&as_sdt=0,39 | 3 | 2,018 |
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies | 69 | neurips | 7 | 2 | 2023-06-15 17:54:36.209000 | https://github.com/srsohn/subtask-graph-execution | 12 | Hierarchical reinforcement learning for zero-shot generalization with subtask dependencies | https://scholar.google.com/scholar?cluster=15468349230439204109&hl=en&as_sdt=0,47 | 2 | 2,018 |
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net | 0 | neurips | 0 | 0 | 2023-06-15 17:54:36.402000 | https://github.com/tmichoel/bayonet | 1 | Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net | https://scholar.google.com/scholar?cluster=14797747024232630376&hl=en&as_sdt=0,39 | 1 | 2,018 |
Streamlining Variational Inference for Constraint Satisfaction Problems | 7 | neurips | 2 | 0 | 2023-06-15 17:54:36.595000 | https://github.com/ermongroup/streamline-vi-csp | 7 | Streamlining variational inference for constraint satisfaction problems | https://scholar.google.com/scholar?cluster=9129978297441572165&hl=en&as_sdt=0,18 | 6 | 2,018 |
Critical initialisation for deep signal propagation in noisy rectifier neural networks | 17 | neurips | 0 | 0 | 2023-06-15 17:54:36.788000 | https://github.com/ElanVB/noisy_signal_prop | 5 | Critical initialisation for deep signal propagation in noisy rectifier neural networks | https://scholar.google.com/scholar?cluster=1536287201347762714&hl=en&as_sdt=0,48 | 6 | 2,018 |
COLA: Decentralized Linear Learning | 117 | neurips | 5 | 0 | 2023-06-15 17:54:36.982000 | https://github.com/epfml/cola | 18 | Cola: Decentralized linear learning | https://scholar.google.com/scholar?cluster=15790148886977326889&hl=en&as_sdt=0,4 | 6 | 2,018 |
A General Method for Amortizing Variational Filtering | 29 | neurips | 8 | 0 | 2023-06-15 17:54:37.176000 | https://github.com/joelouismarino/amortized-variational-filtering | 44 | A general method for amortizing variational filtering | https://scholar.google.com/scholar?cluster=11262711494393358792&hl=en&as_sdt=0,14 | 7 | 2,018 |
One-Shot Unsupervised Cross Domain Translation | 116 | neurips | 17 | 2 | 2023-06-15 17:54:37.372000 | https://github.com/sagiebenaim/OneShotTranslation | 140 | One-shot unsupervised cross domain translation | https://scholar.google.com/scholar?cluster=16456724842379503316&hl=en&as_sdt=0,5 | 6 | 2,018 |
Probabilistic Neural Programmed Networks for Scene Generation | 13 | neurips | 3 | 0 | 2023-06-15 17:54:37.565000 | https://github.com/Lucas2012/ProbabilisticNeuralProgrammedNetwork | 40 | Probabilistic neural programmed networks for scene generation | https://scholar.google.com/scholar?cluster=7658453227892507452&hl=en&as_sdt=0,23 | 5 | 2,018 |
On gradient regularizers for MMD GANs | 94 | neurips | 7 | 0 | 2023-06-15 17:54:37.758000 | https://github.com/MichaelArbel/Scaled-MMD-GAN | 33 | On gradient regularizers for MMD GANs | https://scholar.google.com/scholar?cluster=12044208657387141906&hl=en&as_sdt=0,43 | 6 | 2,018 |
Learning Plannable Representations with Causal InfoGAN | 165 | neurips | 17 | 4 | 2023-06-15 17:54:37.952000 | https://github.com/thanard/causal-infogan | 83 | Learning plannable representations with causal infogan | https://scholar.google.com/scholar?cluster=11334480747970611889&hl=en&as_sdt=0,10 | 15 | 2,018 |
The streaming rollout of deep networks - towards fully model-parallel execution | 12 | neurips | 1 | 0 | 2023-06-15 17:54:38.146000 | https://github.com/boschresearch/statestream | 16 | The streaming rollout of deep networks-towards fully model-parallel execution | https://scholar.google.com/scholar?cluster=4918339413298728627&hl=en&as_sdt=0,10 | 5 | 2,018 |
Generalisation in humans and deep neural networks | 507 | neurips | 21 | 0 | 2023-06-15 17:54:38.339000 | https://github.com/rgeirhos/generalisation-humans-DNNs | 94 | Generalisation in humans and deep neural networks | https://scholar.google.com/scholar?cluster=16577111803298526010&hl=en&as_sdt=0,11 | 8 | 2,018 |
Enhancing the Accuracy and Fairness of Human Decision Making | 37 | neurips | 1 | 0 | 2023-06-15 17:54:38.533000 | https://github.com/Networks-Learning/FairHumanDecisions | 4 | Enhancing the accuracy and fairness of human decision making | https://scholar.google.com/scholar?cluster=9266559070813035929&hl=en&as_sdt=0,36 | 4 | 2,018 |
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models | 33 | neurips | 3 | 0 | 2023-06-15 17:54:38.726000 | https://github.com/neitzal/adaptive-skip-intervals | 25 | Adaptive skip intervals: Temporal abstraction for recurrent dynamical models | https://scholar.google.com/scholar?cluster=7596677518342590575&hl=en&as_sdt=0,5 | 4 | 2,018 |
Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks | 22 | neurips | 1 | 0 | 2023-06-15 17:54:38.920000 | https://github.com/zhangquan-ut/Lomax-delegate-racing-for-survival-analysis-with-competing-risks | 1 | Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks | https://scholar.google.com/scholar?cluster=8420900743499045396&hl=en&as_sdt=0,44 | 1 | 2,018 |
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries | 143 | neurips | 97 | 11 | 2023-06-15 17:54:39.114000 | https://github.com/amirgholami/pyhessian | 538 | Hessian-based analysis of large batch training and robustness to adversaries | https://scholar.google.com/scholar?cluster=4488699145655690539&hl=en&as_sdt=0,44 | 13 | 2,018 |
Bayesian Structure Learning by Recursive Bootstrap | 15 | neurips | 9 | 0 | 2023-06-15 17:54:39.307000 | https://github.com/IntelLabs/causality-lab | 53 | Bayesian structure learning by recursive bootstrap | https://scholar.google.com/scholar?cluster=8741496663210631585&hl=en&as_sdt=0,5 | 10 | 2,018 |
Greedy Hash: Towards Fast Optimization for Accurate Hash Coding in CNN | 141 | neurips | 13 | 1 | 2023-06-15 17:54:39.501000 | https://github.com/ssppp/GreedyHash | 49 | Greedy hash: Towards fast optimization for accurate hash coding in cnn | https://scholar.google.com/scholar?cluster=5080578763427257320&hl=en&as_sdt=0,7 | 2 | 2,018 |
CatBoost: unbiased boosting with categorical features | 1,994 | neurips | 1,127 | 500 | 2023-06-15 17:54:39.694000 | https://github.com/catboost/catboost | 7,187 | CatBoost: unbiased boosting with categorical features | https://scholar.google.com/scholar?cluster=15125594264257209192&hl=en&as_sdt=0,3 | 193 | 2,018 |
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders | 204 | neurips | 54 | 4 | 2023-06-15 17:54:39.888000 | https://github.com/Microsoft/constrained-graph-variational-autoencoder | 202 | Constrained generation of semantically valid graphs via regularizing variational autoencoders | https://scholar.google.com/scholar?cluster=8461416587658034730&hl=en&as_sdt=0,39 | 11 | 2,018 |
Wasserstein Distributionally Robust Kalman Filtering | 74 | neurips | 2 | 0 | 2023-06-15 17:54:40.081000 | https://github.com/sorooshafiee/WKF | 12 | Wasserstein distributionally robust Kalman filtering | https://scholar.google.com/scholar?cluster=3916790984259735894&hl=en&as_sdt=0,44 | 4 | 2,018 |
Recurrently Controlled Recurrent Networks | 22 | neurips | 5 | 0 | 2023-06-15 17:54:40.274000 | https://github.com/vanzytay/NIPS2018_RCRN | 23 | Recurrently controlled recurrent networks | https://scholar.google.com/scholar?cluster=119621077163762339&hl=en&as_sdt=0,22 | 3 | 2,018 |
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data | 26 | neurips | 3 | 0 | 2023-06-15 17:54:40.483000 | https://github.com/OxCSML-BayesNP/HawkesNetOC | 8 | Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data | https://scholar.google.com/scholar?cluster=3157293658449719005&hl=en&as_sdt=0,5 | 5 | 2,018 |
Heterogeneous Multi-output Gaussian Process Prediction | 75 | neurips | 15 | 1 | 2023-06-15 17:54:40.676000 | https://github.com/pmorenoz/HetMOGP | 46 | Heterogeneous multi-output Gaussian process prediction | https://scholar.google.com/scholar?cluster=16326528698943863964&hl=en&as_sdt=0,11 | 6 | 2,018 |
SNIPER: Efficient Multi-Scale Training | 526 | neurips | 449 | 116 | 2023-06-15 17:54:40.870000 | https://github.com/MahyarNajibi/SNIPER | 2,674 | Sniper: Efficient multi-scale training | https://scholar.google.com/scholar?cluster=15792283057349312488&hl=en&as_sdt=0,33 | 84 | 2,018 |
Delta-encoder: an effective sample synthesis method for few-shot object recognition | 329 | neurips | 13 | 0 | 2023-06-15 17:54:41.064000 | https://github.com/EliSchwartz/DeltaEncoder | 50 | Delta-encoder: an effective sample synthesis method for few-shot object recognition | https://scholar.google.com/scholar?cluster=13986746272492724236&hl=en&as_sdt=0,3 | 13 | 2,018 |
Global Gated Mixture of Second-order Pooling for Improving Deep Convolutional Neural Networks | 16 | neurips | 1 | 0 | 2023-06-15 17:54:41.259000 | https://github.com/ZilinGao/GM-SOP | 19 | Global gated mixture of second-order pooling for improving deep convolutional neural networks | https://scholar.google.com/scholar?cluster=12539085796049951238&hl=en&as_sdt=0,44 | 2 | 2,018 |
Neural Code Comprehension: A Learnable Representation of Code Semantics | 213 | neurips | 51 | 11 | 2023-06-15 17:54:41.473000 | https://github.com/spcl/ncc | 184 | Neural code comprehension: A learnable representation of code semantics | https://scholar.google.com/scholar?cluster=9627019893956716634&hl=en&as_sdt=0,5 | 12 | 2,018 |
Structure-Aware Convolutional Neural Networks | 46 | neurips | 4 | 2 | 2023-06-15 17:54:41.666000 | https://github.com/vector-1127/SACNNs | 25 | Structure-aware convolutional neural networks | https://scholar.google.com/scholar?cluster=15143914212740363018&hl=en&as_sdt=0,10 | 4 | 2,018 |
Learning filter widths of spectral decompositions with wavelets | 28 | neurips | 11 | 4 | 2023-06-15 17:54:41.860000 | https://github.com/haidark/WaveletDeconv | 32 | Learning filter widths of spectral decompositions with wavelets | https://scholar.google.com/scholar?cluster=1195090452223114657&hl=en&as_sdt=0,5 | 3 | 2,018 |
BRUNO: A Deep Recurrent Model for Exchangeable Data | 27 | neurips | 7 | 0 | 2023-06-15 17:54:42.053000 | https://github.com/IraKorshunova/bruno | 34 | Bruno: A deep recurrent model for exchangeable data | https://scholar.google.com/scholar?cluster=9358687651511071079&hl=en&as_sdt=0,5 | 6 | 2,018 |
Gaussian Process Prior Variational Autoencoders | 95 | neurips | 10 | 5 | 2023-06-15 17:54:42.247000 | https://github.com/fpcasale/GPPVAE | 67 | Gaussian process prior variational autoencoders | https://scholar.google.com/scholar?cluster=7294538008539835502&hl=en&as_sdt=0,3 | 8 | 2,018 |
Variational Inference with Tail-adaptive f-Divergence | 48 | neurips | 3 | 0 | 2023-06-15 17:54:42.442000 | https://github.com/dilinwang820/adaptive-f-divergence | 20 | Variational inference with tail-adaptive f-divergence | https://scholar.google.com/scholar?cluster=1588246766149700607&hl=en&as_sdt=0,5 | 3 | 2,018 |
Generalizing to Unseen Domains via Adversarial Data Augmentation | 588 | neurips | 20 | 1 | 2023-06-15 17:54:42.635000 | https://github.com/ricvolpi/generalize-unseen-domains | 113 | Generalizing to unseen domains via adversarial data augmentation | https://scholar.google.com/scholar?cluster=3314749587084034699&hl=en&as_sdt=0,33 | 4 | 2,018 |
Isolating Sources of Disentanglement in Variational Autoencoders | 1,085 | neurips | 70 | 1 | 2023-06-15 17:54:42.830000 | https://github.com/rtqichen/beta-tcvae | 311 | Isolating sources of disentanglement in variational autoencoders | https://scholar.google.com/scholar?cluster=11372263911361899725&hl=en&as_sdt=0,5 | 12 | 2,018 |
Learning to Share and Hide Intentions using Information Regularization | 58 | neurips | 6 | 0 | 2023-06-15 17:54:43.023000 | https://github.com/djstrouse/InfoMARL | 19 | Learning to share and hide intentions using information regularization | https://scholar.google.com/scholar?cluster=17666377994780351102&hl=en&as_sdt=0,18 | 5 | 2,018 |
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks | 804 | neurips | 19 | 5 | 2023-06-15 17:54:43.216000 | https://github.com/ashafahi/inceptionv3-transferLearn-poison | 50 | Poison frogs! targeted clean-label poisoning attacks on neural networks | https://scholar.google.com/scholar?cluster=2909175979109217787&hl=en&as_sdt=0,5 | 4 | 2,018 |
Non-metric Similarity Graphs for Maximum Inner Product Search | 57 | neurips | 10 | 1 | 2023-06-15 17:54:43.410000 | https://github.com/stanis-morozov/ip-nsw | 38 | Non-metric similarity graphs for maximum inner product search | https://scholar.google.com/scholar?cluster=7566476240574710197&hl=en&as_sdt=0,49 | 5 | 2,018 |
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction | 125 | neurips | 22 | 6 | 2023-06-15 17:54:43.603000 | https://github.com/shikorab/SceneGraph | 68 | Mapping images to scene graphs with permutation-invariant structured prediction | https://scholar.google.com/scholar?cluster=10299834729999374704&hl=en&as_sdt=0,39 | 9 | 2,018 |
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration | 767 | neurips | 501 | 318 | 2023-06-15 17:54:43.797000 | https://github.com/cornellius-gp/gpytorch | 3,139 | Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration | https://scholar.google.com/scholar?cluster=15805506961047915622&hl=en&as_sdt=0,25 | 55 | 2,018 |
Attention in Convolutional LSTM for Gesture Recognition | 105 | neurips | 51 | 19 | 2023-06-15 17:54:43.991000 | https://github.com/GuangmingZhu/AttentionConvLSTM | 205 | Attention in convolutional LSTM for gesture recognition | https://scholar.google.com/scholar?cluster=13184940893185979866&hl=en&as_sdt=0,47 | 4 | 2,018 |
Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger | 29 | neurips | 5 | 2 | 2023-06-15 17:54:44.186000 | https://github.com/facebookresearch/starcraft_defogger | 30 | Forward modeling for partial observation strategy games-a starcraft defogger | https://scholar.google.com/scholar?cluster=5562179615762953081&hl=en&as_sdt=0,5 | 13 | 2,018 |
PacGAN: The power of two samples in generative adversarial networks | 312 | neurips | 9 | 1 | 2023-06-15 17:54:44.379000 | https://github.com/fjxmlzn/PacGAN | 80 | Pacgan: The power of two samples in generative adversarial networks | https://scholar.google.com/scholar?cluster=14705983068913748289&hl=en&as_sdt=0,5 | 4 | 2,018 |
Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages | 30 | neurips | 2 | 0 | 2023-06-15 17:54:44.573000 | https://github.com/forest-snow/mtanchor_demo | 9 | Multilingual anchoring: Interactive topic modeling and alignment across languages | https://scholar.google.com/scholar?cluster=12128120271299187435&hl=en&as_sdt=0,5 | 1 | 2,018 |
Sanity Checks for Saliency Maps | 1,530 | neurips | 13 | 11 | 2023-06-15 17:54:44.767000 | https://github.com/adebayoj/sanity_checks_saliency | 99 | Sanity checks for saliency maps | https://scholar.google.com/scholar?cluster=8767887416569707674&hl=en&as_sdt=0,41 | 10 | 2,018 |
Deep Dynamical Modeling and Control of Unsteady Fluid Flows | 126 | neurips | 19 | 0 | 2023-06-15 17:54:44.961000 | https://github.com/sisl/deep_flow_control | 38 | Deep dynamical modeling and control of unsteady fluid flows | https://scholar.google.com/scholar?cluster=8193012965395960760&hl=en&as_sdt=0,33 | 6 | 2,018 |
Lifelong Inverse Reinforcement Learning | 13 | neurips | 3 | 0 | 2023-06-15 17:54:45.154000 | https://github.com/lifelong-ml/elirl | 7 | Lifelong inverse reinforcement learning | https://scholar.google.com/scholar?cluster=8930935480048739276&hl=en&as_sdt=0,10 | 5 | 2,018 |
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks | 83 | neurips | 4 | 0 | 2023-06-15 17:54:45.350000 | https://github.com/popgenmethods/defiNETti | 18 | A likelihood-free inference framework for population genetic data using exchangeable neural networks | https://scholar.google.com/scholar?cluster=5564729426311739157&hl=en&as_sdt=0,5 | 4 | 2,018 |
Inferring Networks From Random Walk-Based Node Similarities | 7 | neurips | 13 | 0 | 2023-06-15 17:54:45.543000 | https://github.com/cnmusco/graph-similarity-learning | 30 | Inferring networks from random walk-based node similarities | https://scholar.google.com/scholar?cluster=4035487172765819261&hl=en&as_sdt=0,34 | 9 | 2,018 |
Distributed $k$-Clustering for Data with Heavy Noise | 22 | neurips | 1 | 0 | 2023-06-15 17:54:45.737000 | https://github.com/xyguo/clusterz | 9 | Distributed -Clustering for Data with Heavy Noise | https://scholar.google.com/scholar?cluster=4052545958640287143&hl=en&as_sdt=0,5 | 1 | 2,018 |
Deepcode: Feedback Codes via Deep Learning | 91 | neurips | 12 | 1 | 2023-06-15 17:54:45.930000 | https://github.com/hyejikim1/Deepcode | 15 | Deepcode: Feedback codes via deep learning | https://scholar.google.com/scholar?cluster=17328761776643473390&hl=en&as_sdt=0,31 | 4 | 2,018 |
Hamiltonian Variational Auto-Encoder | 82 | neurips | 2 | 0 | 2023-06-15 17:54:46.123000 | https://github.com/anthonycaterini/hvae-nips | 14 | Hamiltonian variational auto-encoder | https://scholar.google.com/scholar?cluster=13199503496722173919&hl=en&as_sdt=0,6 | 2 | 2,018 |
Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations | 23 | neurips | 0 | 1 | 2023-06-15 17:54:46.317000 | https://github.com/wangtongada/MRS | 3 | Multi-value rule sets for interpretable classification with feature-efficient representations | https://scholar.google.com/scholar?cluster=13805737803480413432&hl=en&as_sdt=0,23 | 3 | 2,018 |
ATOMO: Communication-efficient Learning via Atomic Sparsification | 302 | neurips | 5 | 2 | 2023-06-15 17:54:46.510000 | https://github.com/hwang595/ATOMO | 23 | Atomo: Communication-efficient learning via atomic sparsification | https://scholar.google.com/scholar?cluster=8287483998499358971&hl=en&as_sdt=0,26 | 2 | 2,018 |
Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models | 21 | neurips | 5 | 0 | 2023-06-15 17:54:46.703000 | https://github.com/amber0309/ANM-MM | 16 | Causal inference and mechanism clustering of a mixture of additive noise models | https://scholar.google.com/scholar?cluster=17153751836211673378&hl=en&as_sdt=0,5 | 2 | 2,018 |
Scaling provable adversarial defenses | 417 | neurips | 83 | 8 | 2023-06-15 17:54:46.897000 | https://github.com/locuslab/convex_adversarial | 357 | Scaling provable adversarial defenses | https://scholar.google.com/scholar?cluster=17860970585851528849&hl=en&as_sdt=0,33 | 16 | 2,018 |
DropMax: Adaptive Variational Softmax | 14 | neurips | 2 | 0 | 2023-06-15 17:54:47.091000 | https://github.com/haebeom-lee/dropmax | 18 | DropMax: Adaptive variational softmax | https://scholar.google.com/scholar?cluster=6113755016125254061&hl=en&as_sdt=0,5 | 1 | 2,018 |
Automatic Program Synthesis of Long Programs with a Learned Garbage Collector | 69 | neurips | 14 | 0 | 2023-06-15 17:54:47.284000 | https://github.com/amitz25/PCCoder | 44 | Automatic program synthesis of long programs with a learned garbage collector | https://scholar.google.com/scholar?cluster=8202429186928135403&hl=en&as_sdt=0,11 | 3 | 2,018 |
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions | 168 | neurips | 4 | 3 | 2023-06-15 17:54:47.478000 | https://github.com/caus-am/dom_adapt | 17 | Domain adaptation by using causal inference to predict invariant conditional distributions | https://scholar.google.com/scholar?cluster=3967372382720766256&hl=en&as_sdt=0,5 | 7 | 2,018 |
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes | 43 | neurips | 5 | 0 | 2023-06-15 17:54:47.672000 | https://github.com/RonanFR/UCRL | 25 | Near optimal exploration-exploitation in non-communicating Markov decision processes | https://scholar.google.com/scholar?cluster=6645061695976054329&hl=en&as_sdt=0,39 | 5 | 2,018 |
Mesh-TensorFlow: Deep Learning for Supercomputers | 301 | neurips | 248 | 98 | 2023-06-15 17:54:47.865000 | https://github.com/tensorflow/mesh | 1,427 | Mesh-tensorflow: Deep learning for supercomputers | https://scholar.google.com/scholar?cluster=1887735754811341119&hl=en&as_sdt=0,18 | 48 | 2,018 |
Semi-crowdsourced Clustering with Deep Generative Models | 19 | neurips | 4 | 1 | 2023-06-15 17:54:48.058000 | https://github.com/xinmei9322/semicrowd | 10 | Semi-crowdsourced clustering with deep generative models | https://scholar.google.com/scholar?cluster=5214247288739307462&hl=en&as_sdt=0,6 | 3 | 2,018 |
Scalable Laplacian K-modes | 10 | neurips | 1 | 0 | 2023-06-15 17:54:48.252000 | https://github.com/imtiazziko/SLK | 10 | Scalable laplacian K-modes | https://scholar.google.com/scholar?cluster=673736975875501078&hl=en&as_sdt=0,5 | 1 | 2,018 |
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models | 1,017 | neurips | 92 | 11 | 2023-06-15 17:54:48.453000 | https://github.com/kchua/handful-of-trials | 397 | Deep reinforcement learning in a handful of trials using probabilistic dynamics models | https://scholar.google.com/scholar?cluster=6248399848380977147&hl=en&as_sdt=0,44 | 15 | 2,018 |
Inexact trust-region algorithms on Riemannian manifolds | 20 | neurips | 5 | 0 | 2023-06-15 17:54:48.647000 | https://github.com/hiroyuki-kasai/Subsampled-RTR | 6 | Inexact trust-region algorithms on Riemannian manifolds | https://scholar.google.com/scholar?cluster=197435474681214281&hl=en&as_sdt=0,6 | 2 | 2,018 |
Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks | 187 | neurips | 18 | 0 | 2023-06-15 17:54:48.841000 | https://github.com/jinyyy666/mm-bp-snn | 33 | Hybrid macro/micro level backpropagation for training deep spiking neural networks | https://scholar.google.com/scholar?cluster=6794497534863732123&hl=en&as_sdt=0,7 | 4 | 2,018 |
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance | 103 | neurips | 1 | 0 | 2023-06-15 17:54:49.034000 | https://github.com/GiulsLu/OT-gradients | 10 | Differential properties of sinkhorn approximation for learning with wasserstein distance | https://scholar.google.com/scholar?cluster=436330101781594143&hl=en&as_sdt=0,5 | 2 | 2,018 |
Processing of missing data by neural networks | 122 | neurips | 11 | 1 | 2023-06-15 17:54:49.228000 | https://github.com/lstruski/Processing-of-missing-data-by-neural-networks | 38 | Processing of missing data by neural networks | https://scholar.google.com/scholar?cluster=8626650856385111699&hl=en&as_sdt=0,5 | 0 | 2,018 |
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo | 91 | neurips | 10 | 1 | 2023-06-15 17:54:49.421000 | https://github.com/cambridge-mlg/sghmc_dgp | 26 | Inference in deep Gaussian processes using stochastic gradient Hamiltonian Monte Carlo | https://scholar.google.com/scholar?cluster=3764755113585298283&hl=en&as_sdt=0,15 | 7 | 2,018 |
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior | 35 | neurips | 1 | 0 | 2023-06-15 17:54:49.615000 | https://github.com/beomjoonkim/MetaLearnBO | 8 | Regret bounds for meta bayesian optimization with an unknown gaussian process prior | https://scholar.google.com/scholar?cluster=17688880368262090655&hl=en&as_sdt=0,33 | 4 | 2,018 |
Large Margin Deep Networks for Classification | 249 | neurips | 7,319 | 1,025 | 2023-06-15 17:54:49.809000 | https://github.com/google-research/google-research | 29,774 | Large margin deep networks for classification | https://scholar.google.com/scholar?cluster=4375455714147672635&hl=en&as_sdt=0,5 | 727 | 2,018 |
Multi-Task Learning as Multi-Objective Optimization | 797 | neurips | 154 | 17 | 2023-06-15 17:54:50.003000 | https://github.com/IntelVCL/MultiObjectiveOptimization | 768 | Multi-task learning as multi-objective optimization | https://scholar.google.com/scholar?cluster=7092916310292802870&hl=en&as_sdt=0,5 | 19 | 2,018 |
Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch | 91 | neurips | 9 | 0 | 2023-06-15 17:54:50.197000 | https://github.com/OsmanMalik/tucker-tensorsketch | 22 | Low-rank tucker decomposition of large tensors using tensorsketch | https://scholar.google.com/scholar?cluster=14930463506395433719&hl=en&as_sdt=0,47 | 3 | 2,018 |
But How Does It Work in Theory? Linear SVM with Random Features | 55 | neurips | 1 | 1 | 2023-06-15 17:54:50.391000 | https://github.com/syitong/randfourier | 4 | But how does it work in theory? Linear SVM with random features | https://scholar.google.com/scholar?cluster=2923305469042609420&hl=en&as_sdt=0,5 | 4 | 2,018 |
A Probabilistic U-Net for Segmentation of Ambiguous Images | 437 | neurips | 96 | 15 | 2023-06-15 17:54:50.584000 | https://github.com/SimonKohl/probabilistic_unet | 518 | A probabilistic u-net for segmentation of ambiguous images | https://scholar.google.com/scholar?cluster=17567416838130660215&hl=en&as_sdt=0,11 | 20 | 2,018 |
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks | 246 | neurips | 7 | 1 | 2023-06-15 17:54:50.778000 | https://github.com/ytsmiling/lmt | 34 | Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks | https://scholar.google.com/scholar?cluster=17946280354894784321&hl=en&as_sdt=0,44 | 2 | 2,018 |
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data | 378 | neurips | 43 | 4 | 2023-06-15 17:54:50.972000 | https://github.com/mariogeiger/se3cnn | 181 | 3d steerable cnns: Learning rotationally equivariant features in volumetric data | https://scholar.google.com/scholar?cluster=10898598436815000986&hl=en&as_sdt=0,5 | 10 | 2,018 |
Reducing Network Agnostophobia | 233 | neurips | 11 | 2 | 2023-06-15 17:54:51.165000 | https://github.com/Vastlab/Reducing-Network-Agnostophobia | 63 | Reducing network agnostophobia | https://scholar.google.com/scholar?cluster=13549236386686072567&hl=en&as_sdt=0,36 | 11 | 2,018 |
Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions | 34 | neurips | 7 | 1 | 2023-06-15 17:54:51.359000 | https://github.com/mhsung/deep-functional-dictionaries | 38 | Deep functional dictionaries: Learning consistent semantic structures on 3d models from functions | https://scholar.google.com/scholar?cluster=9622270934005244916&hl=en&as_sdt=0,23 | 3 | 2,018 |
Learning to Decompose and Disentangle Representations for Video Prediction | 284 | neurips | 24 | 3 | 2023-06-15 17:54:51.553000 | https://github.com/jthsieh/DDPAE-video-prediction | 133 | Learning to decompose and disentangle representations for video prediction | https://scholar.google.com/scholar?cluster=3026670262984428356&hl=en&as_sdt=0,23 | 7 | 2,018 |
Moonshine: Distilling with Cheap Convolutions | 115 | neurips | 5 | 1 | 2023-06-15 17:54:51.751000 | https://github.com/BayesWatch/pytorch-moonshine | 33 | Moonshine: Distilling with cheap convolutions | https://scholar.google.com/scholar?cluster=1198937430039662694&hl=en&as_sdt=0,37 | 4 | 2,018 |
Learning Conditioned Graph Structures for Interpretable Visual Question Answering | 231 | neurips | 35 | 6 | 2023-06-15 17:54:51.945000 | https://github.com/aimbrain/vqa-project | 146 | Learning conditioned graph structures for interpretable visual question answering | https://scholar.google.com/scholar?cluster=16899155560172978534&hl=en&as_sdt=0,21 | 9 | 2,018 |
Temporal Regularization for Markov Decision Process | 23 | neurips | 1 | 0 | 2023-06-15 17:54:52.138000 | https://github.com/pierthodo/temporal_regularization | 6 | Temporal regularization for markov decision process | https://scholar.google.com/scholar?cluster=12308924458627658967&hl=en&as_sdt=0,6 | 4 | 2,018 |
Meta-Reinforcement Learning of Structured Exploration Strategies | 322 | neurips | 7 | 2 | 2023-06-15 17:54:52.332000 | https://github.com/russellmendonca/maesn_suite | 39 | Meta-reinforcement learning of structured exploration strategies | https://scholar.google.com/scholar?cluster=8837867565687609361&hl=en&as_sdt=0,44 | 4 | 2,018 |
Unsupervised Attention-guided Image-to-Image Translation | 320 | neurips | 49 | 21 | 2023-06-15 17:54:52.525000 | https://github.com/AlamiMejjati/Unsupervised-Attention-guided-Image-to-Image-Translation | 322 | Unsupervised attention-guided image-to-image translation | https://scholar.google.com/scholar?cluster=912464851779595905&hl=en&as_sdt=0,48 | 11 | 2,018 |
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