Diederik P. Kingma

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Tim SalimansGoogle DeepMind AmsterdamVerified email at google.com
Jimmy BaUniversity of TorontoVerified email at cs.toronto.edu
Max WellingCAIO CuspAI & Professor Machine Learning, University of AmsterdamVerified email at uva.nl
Prafulla DhariwalResearcher, OpenAIVerified email at openai.com
Ben PooleResearch Scientist at Google DeepMindVerified email at stanford.edu
Jascha Sohl-DicksteinAnthropicVerified email at anthropic.com
RUIQI GAOResearch Scientist, Google DeepMindVerified email at google.com
(Peter) Xi Chencovariant.ai | UC BerkeleyVerified email at berkeley.edu
Jonathan HoVerified email at berkeley.edu
Yang SongOpenAIVerified email at openai.com
Ilya SutskeverCo-Founder and Chief Scientist of OpenAIVerified email at openai.com
Stefano ErmonStanford UniversityVerified email at cs.stanford.edu
Abhishek KumarGoogle BrainVerified email at google.com
David J FleetGoogle DeepMind; University of Toronto; Vector InstituteVerified email at cs.toronto.edu
Shakir MohamedResearch Director, Google DeepMindVerified email at deepmind.com
Danilo J. RezendeDirector 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
Alexey A. GritsenkoGoogle DeepMindVerified email at google.com

Diederik P. Kingma

Other namesDurk Kingma, Diederik Pieter Kingma

Research Scientist, Google Brain

Verified email at google.com - Homepage

Machine Learning Deep Learning Neural Networks Generative Models Artificial Intelligence


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 arXiv preprint arXiv:1412.6980, 2014	198894	2014
Auto-Encoding Variational Bayes DP Kingma, M Welling arXiv preprint arXiv:1312.6114, 2013	40126	2013
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	4827	2020
Semi-Supervised Learning with Deep Generative Models DP Kingma, S Mohamed, DJ Rezende, M Welling Advances in Neural Information Processing Systems, 3581-3589, 2014	3650	2014
Glow: Generative Flow with Invertible 1x1 Convolutions DP Kingma, P Dhariwal Advances in Neural Information Processing Systems, 10215-10224, 2018	3481	2018
An Introduction to Variational Autoencoders DP Kingma, M Welling Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019	3044	2019
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	2337	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	2163	2016
Variational Dropout and the Local Reparameterization Trick DP Kingma, T Salimans, M Welling Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015	1781	2015
Learning Sparse Neural Networks through Regularization C Louizos, M Welling, DP Kingma Proceedings of the International Conference on Learning Representations (ICLR), 2017	1273	2017
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	1161	2017
Imagen video: High definition video generation with diffusion models J Ho, W Chan, C Saharia, J Whang, R Gao, A Gritsenko, DP Kingma, ... arXiv preprint arXiv:2210.02303, 2022	1154	2022
Variational Diffusion Models D Kingma, T Salimans, B Poole, J Ho Advances in neural information processing systems 34, 21696-21707, 2021	952	2021
Variational Lossy Autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016	818	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	726	2014
3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings DP Kingma, J Ba, Y Bengio, Y LeCun arXiv preprint arXiv:1412.6980, 2015	643*	2015
A method for stochastic optimization. Int. Conf. Learn DP Kingma, JLB Adam Representations (ICLR) 6, 2015	633	2015
Variational Autoencoders and Nonlinear ICA: A Unifying Framework I Khemakhem, DP Kingma, A Hyvärinen The 23rd International Conference on Artificial Intelligence and Statistics …, 2019	613	2019
Adam: A method for stochastic optimization 3rd International Conference on Learning Representations DP Kingma, JL Ba ICLR 2015-Conference Track Proceedings 1, 2015	392	2015
On distillation of guided diffusion models C Meng, R Rombach, R Gao, D Kingma, S Ermon, J Ho, T Salimans Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023	341	2023

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