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
Shakir MohamedSenior Staff Scientist, DeepMindVerified email at deepmind.com
Danilo J. RezendeSenior Staff Research Scientist at DeepMindVerified email at google.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
Ben PooleResearch Scientist at Google BrainVerified email at stanford.edu
Pieter AbbeelUC Berkeley | Covariant.AIVerified email at cs.berkeley.edu
John SchulmanResearch Scientist, OpenAIVerified email at openai.com
Yang Song (宋飏)Stanford UniversityVerified email at cs.stanford.edu
Christos LouizosQualcomm AI ResearchVerified email at qti.qualcomm.com
Jascha Sohl-DicksteinGoogle BrainVerified email at google.com
Yan DuanCovariant.AIVerified email at covariant.ai
Jonathan HoGoogle BrainVerified email at google.com
Sergey LevineUC Berkeley, GoogleVerified email at eecs.berkeley.edu

Diederik P. 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	118145	2014
Auto-Encoding Variational Bayes DP Kingma, M Welling arXiv preprint arXiv:1312.6114, 2013	22326	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	2645	2014
Glow: Generative Flow with Invertible 1x1 Convolutions DP Kingma, P Dhariwal Advances in Neural Information Processing Systems, 10215-10224, 2018	1827	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	1550	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	1485	2016
Variational Dropout and the Local Reparameterization Trick DP Kingma, T Salimans, M Welling Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015	1153	2015
An Introduction to Variational Autoencoders DP Kingma, M Welling Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019	987	2019
Adam: A Method for Stochastic Optimization.(2014). arXiv DP Kingma, J Ba arXiv preprint arXiv:1412.6980, 2014	807*	2014
Adam: a method for stochastic optimization. arXiv preprint (2014) DP Kingma, J Ba arXiv preprint arXiv:1412.6980 2, 2014	760*	2014
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	751	2017
Learning Sparse Neural Networks through Regularization C Louizos, M Welling, DP Kingma Proceedings of the International Conference on Learning Representations (ICLR), 2017	740	2017
Adam: A method for stochastic optimization. CoRR abs/1412.6980 DP Kingma, J Ba	635	2014
Variational Lossy Autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016	614	2016
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	544	2014
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	278	2020
Adam: A method for stochastic optimization. arXiv: 14126980 DP Kingma, J Ba	240	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	239	2019
Adam: A method for stochastic optimization B Jimmy, P Diederik arXiv preprint arXiv: 1412.6980, 2014	208*	2014
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	161*	2019

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