Diederik P. Kingma

Cited by

	All	Since 2018
Citations	194837	184161
h-index	35	34
i10-index	39	39

46000

23000

11500

34500

201620172018201920202021202220232145 7027 15793 25646 34160 42567 45646 20343

Public access

View all

3 articles

0 articles

available

not available

Based on funding mandates

Co-authors

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
RUIQI GAOResearch Scientist, Google BrainVerified email at google.com
Shakir MohamedSenior Staff Scientist, DeepMindVerified email at deepmind.com
Danilo J. RezendeSenior Staff Research Scientist at DeepMindVerified 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	148267	2014
Auto-Encoding Variational Bayes DP Kingma, M Welling arXiv preprint arXiv:1312.6114, 2013	27820	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	3019	2014
Glow: Generative Flow with Invertible 1x1 Convolutions DP Kingma, P Dhariwal Advances in Neural Information Processing Systems, 10215-10224, 2018	2425	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	1859	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	1740	2016
An Introduction to Variational Autoencoders DP Kingma, M Welling Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019	1580	2019
Variational Dropout and the Local Reparameterization Trick DP Kingma, T Salimans, M Welling Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015	1390	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	1133	2020
Learning Sparse Neural Networks through Regularization C Louizos, M Welling, DP Kingma Proceedings of the International Conference on Learning Representations (ICLR), 2017	922	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	919	2017
Variational Lossy Autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016	683	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	625	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	352	2019
Variational Diffusion Models D Kingma, T Salimans, B Poole, J Ho Advances in neural information processing systems 34, 21696-21707, 2021	292	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
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	161	2022
GPU Kernels for Block-Sparse Weights S Gray, A Radford, DP Kingma	155	2017
How to train your energy-based models Y Song, DP Kingma arXiv preprint arXiv:2101.03288, 2021	124	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	86	2020

The system can't perform the operation now. Try again later.

Articles 1–20

Citations per year

Duplicate citations

Merged citations

Add co-authorsCo-authors

Follow

Cited by

Co-authors