Diederik P. Kingma

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Jimmy BaUniversity of TorontoVerified email at cs.toronto.edu
Max WellingCAIO CuspAI & Professor Machine Learning, University of AmsterdamVerified email at uva.nl
Tim SalimansGoogle DeepMind AmsterdamVerified email at google.com
Prafulla DhariwalResearcher, OpenAIVerified email at openai.com
Ben PooleResearch Scientist at Google DeepMindVerified email at stanford.edu
Stefano ErmonStanford UniversityVerified email at cs.stanford.edu
Yang SongOpenAIVerified email at openai.com
Jascha Sohl-DicksteinAnthropicVerified email at anthropic.com
(Peter) Xi Chencovariant.ai | UC BerkeleyVerified email at berkeley.edu
RUIQI GAOResearch Scientist, Google DeepMindVerified email at google.com
Ilya SutskeverCo-Founder and Chief Scientist at Safe Superintelligence IncVerified email at ssi.inc
Jonathan HoVerified email at berkeley.edu
Abhishek KumarGoogle BrainVerified email at google.com
Shakir MohamedResearch Director, Google DeepMindVerified email at deepmind.com
Danilo Jimenez RezendeEllison Institute of TechnologyVerified email at eit.org
Yann LeCunChief AI Scientist at Facebook & JT Schwarz Professor at the Courant Institute, New York UniversityVerified email at cs.nyu.edu
Aapo HyvärinenUniversity 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
Christos LouizosQualcomm AI ResearchVerified email at qti.qualcomm.com

Diederik P. Kingma

Other namesDurk Kingma, Diederik Pieter Kingma

Anthropic

Verified email at anthropic.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	215042	2014
Auto-Encoding Variational Bayes DP Kingma, M Welling arXiv preprint arXiv:1312.6114, 2013	44683	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	6608	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	3841	2014
Glow: Generative Flow with Invertible 1x1 Convolutions DP Kingma, P Dhariwal Advances in Neural Information Processing Systems, 10215-10224, 2018	3818	2018
An Introduction to Variational Autoencoders DP Kingma, M Welling Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019	3531	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	2453	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	2303	2016
Variational Dropout and the Local Reparameterization Trick DP Kingma, T Salimans, M Welling Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015	1901	2015
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	1406*	2015
Learning Sparse Neural Networks through Regularization C Louizos, M Welling, DP Kingma Proceedings of the International Conference on Learning Representations (ICLR), 2017	1397	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	1252	2017
Variational Diffusion Models D Kingma, T Salimans, B Poole, J Ho Advances in neural information processing systems 34, 21696-21707, 2021	1189	2021
Variational Lossy Autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016	863	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	757	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	696	2019
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	509	2023
Adam: a method for stochastic optimization. arXiv e-prints DP Kingma, J Ba arXiv preprint arXiv:1412.6980 1412, 2014	412	2014
How to train your energy-based models Y Song, DP Kingma arXiv preprint arXiv:2101.03288, 2021	315	2021
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	268*	2019

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