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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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