Diederik P. Kingma

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Jimmy BaUniversity of TorontoVerified email at cs.toronto.edu
Max WellingDistinguished Scientist at MSR AI4Science & Professor 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
Ben PooleResearch Scientist at Google BrainVerified email at stanford.edu
Ilya SutskeverCo-Founder and Chief Scientist of OpenAIVerified email at openai.com
Jonathan HoVerified email at berkeley.edu
Shakir MohamedSenior Staff Scientist, DeepMindVerified email at deepmind.com
Danilo J. RezendeSenior Staff Research Scientist at DeepMindVerified email at google.com
RUIQI GAOResearch Scientist, Google BrainVerified email at google.com
Yang SongOpenAIVerified email at cs.stanford.edu
Stefano ErmonStanford UniversityVerified email at cs.stanford.edu
Jascha Sohl-DicksteinGoogle BrainVerified 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
Pieter AbbeelUC Berkeley | Covariant.AIVerified email at cs.berkeley.edu
John SchulmanResearch Scientist, OpenAIVerified email at openai.com
Abhishek KumarGoogle BrainVerified email at google.com

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	147156	2014
Auto-Encoding Variational Bayes DP Kingma, M Welling arXiv preprint arXiv:1312.6114, 2013	27565	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	3012	2014
Glow: Generative Flow with Invertible 1x1 Convolutions DP Kingma, P Dhariwal Advances in Neural Information Processing Systems, 10215-10224, 2018	2389	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	1831	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	1728	2016
An Introduction to Variational Autoencoders DP Kingma, M Welling Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019	1548	2019
Variational Dropout and the Local Reparameterization Trick DP Kingma, T Salimans, M Welling Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015	1375	2015
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	1057	2020
Learning Sparse Neural Networks through Regularization C Louizos, M Welling, DP Kingma Proceedings of the International Conference on Learning Representations (ICLR), 2017	913	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	912	2017
Variational Lossy Autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016	682	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	621	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	346	2019
Variational Diffusion Models D Kingma, T Salimans, B Poole, J Ho Advances in neural information processing systems 34, 21696-21707, 2021	267	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	193*	2019
GPU Kernels for Block-Sparse Weights S Gray, A Radford, DP Kingma	153	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	146	2022
How to train your energy-based models Y Song, DP Kingma arXiv preprint arXiv:2101.03288, 2021	118	2021
Flow contrastive estimation of energy-based models R Gao, E Nijkamp, DP Kingma, Z Xu, AM Dai, YN Wu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020	84	2020

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