Diederik P. Kingma

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Jimmy BaUniversity of TorontoVerified email at cs.toronto.edu
Max WellingProfessor Machine Learning, University of AmsterdamVerified email at uva.nl
Tim SalimansGoogle Brain AmsterdamVerified email at google.com
Prafulla DhariwalResearcher, OpenAIVerified email at openai.com
(Peter) Xi Chencovariant.ai | UC BerkeleyVerified email at berkeley.edu
Ilya SutskeverCo-Founder and Chief Scientist of OpenAIVerified email at openai.com
Ben PooleResearch Scientist at Google BrainVerified email at stanford.edu
Shakir MohamedSenior Staff Scientist, DeepMindVerified email at deepmind.com
Danilo J. RezendeSenior Staff Research Scientist at DeepMindVerified email at google.com
Jonathan HoVerified email at berkeley.edu
Yang SongOpenAIVerified email at cs.stanford.edu
Pieter AbbeelUC Berkeley | Covariant.AIVerified email at cs.berkeley.edu
John SchulmanResearch Scientist, OpenAIVerified email at openai.com
Aapo HyvarinenUniversity of HelsinkiVerified email at helsinki.fi
Ilyes KhemakhemPhD candidate, Gatsby Unit, UCLVerified email at gatsby.ucl.ac.uk
Ricardo Pio MontiGatsby Unit, UCLVerified email at gatsby.ucl.ac.uk
Stefano ErmonStanford UniversityVerified email at cs.stanford.edu
Jascha Sohl-DicksteinGoogle BrainVerified email at google.com
RUIQI GAOResearch Scientist, Google BrainVerified email at google.com
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityVerified email at cs.nyu.edu

Diederik P. Kingma

Other namesDurk Kingma

Research Scientist, Google Brain

Verified email at google.com - Homepage

Machine Learning Deep Learning Neural Networks Generative Models Variational Inference


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Adam: A Method for Stochastic Optimization DP Kingma, J Ba Proceedings of the 3rd International Conference on Learning Representations …, 2014	135324	2014
Auto-Encoding Variational Bayes DP Kingma, M Welling arXiv preprint arXiv:1312.6114, 2013	25273	2013
Semi-Supervised Learning with Deep Generative Models DP Kingma, S Mohamed, DJ Rezende, M Welling Advances in Neural Information Processing Systems, 3581-3589, 2014	2860	2014
Glow: Generative Flow with Invertible 1x1 Convolutions DP Kingma, P Dhariwal Advances in Neural Information Processing Systems, 10215-10224, 2018	2132	2018
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks T Salimans, DP Kingma Advances in Neural Information Processing Systems, 901-901, 2016	1707	2016
Improved Variational Inference with Inverse Autoregressive Flow DP Kingma, T Salimans, R Jozefowicz, X Chen, I Sutskever, M Welling Advances in Neural Information Processing Systems, 4743-4751, 2016	1621	2016
Variational Dropout and the Local Reparameterization Trick DP Kingma, T Salimans, M Welling Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015	1298	2015
An Introduction to Variational Autoencoders DP Kingma, M Welling Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019	1290	2019
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications T Salimans, A Karpathy, X Chen, DP Kingma arXiv preprint arXiv:1701.05517, 2017	843	2017
Learning Sparse Neural Networks through Regularization C Louizos, M Welling, DP Kingma Proceedings of the International Conference on Learning Representations (ICLR), 2017	837	2017
Variational Lossy Autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016	652	2016
Score-based generative modeling through stochastic differential equations Y Song, J Sohl-Dickstein, DP Kingma, A Kumar, S Ermon, B Poole arXiv preprint arXiv:2011.13456, 2020	607	2020
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap T Salimans, DP Kingma, M Welling Proceedings of the International Conference on Machine Learning (ICML), 2014	581	2014
Variational Autoencoders and Nonlinear ICA: A Unifying Framework I Khemakhem, DP Kingma, A Hyvärinen The 23rd International Conference on Artificial Intelligence and Statistics …, 2019	300	2019
A method for stochastic optimization. arXiv preprint 2014 DP Kingma, BJ Adam arXiv preprint arXiv:1412.6980 205, 2014	277	2014
International Conference on Learning Representations DP Kingma, J Ba 3rd International Conference on Learning Representations. ICLR 2015 …, 2015	237	2015
VideoFlow: A Flow-Based Generative Model for Video M Kumar, M Babaeizadeh, D Erhan, C Finn, S Levine, L Dinh, DP Kingma Proceedings of the International Conference on Learning Representations (ICLR), 2019	182*	2019
Variational Diffusion Models D Kingma, T Salimans, B Poole, J Ho Advances in neural information processing systems 34, 21696-21707, 2021	166	2021
GPU Kernels for Block-Sparse Weights S Gray, A Radford, DP Kingma	143	2017
A MethodforStochasticOptimizaG tion DP Kingma, BAJ Adam Proceedingsofthe3rdInternationalConferenceon LearningRepresentations, 2015	121	2015

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