Diederik P. Kingma

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Citations	248027	221304
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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
Ben PooleResearch Scientist at Google DeepMindVerified email at stanford.edu
(Peter) Xi Chencovariant.ai | UC BerkeleyVerified email at berkeley.edu
Jascha Sohl-DicksteinAnthropicVerified email at anthropic.com
Jonathan HoVerified email at berkeley.edu
RUIQI GAOResearch Scientist, Google BrainVerified email at google.com
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
Shakir MohamedResearch Director, Google DeepMindVerified email at deepmind.com
Danilo J. RezendeDirector at DeepMindVerified email at google.com
David J FleetGoogle DeepMind; University of Toronto; Vector InstituteVerified email at cs.toronto.edu
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 | CovariantVerified email at cs.berkeley.edu

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


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EM Distillation for One-step Diffusion Models S Xie, Z Xiao, DP Kingma, T Hou, YN Wu, KP Murphy, T Salimans, ... arXiv preprint arXiv:2405.16852, 2024		2024
Diffusion Models Having Improved Accuracy and Reduced Consumption of Computational Resources DP Kingma, T Salimans US Patent App. 18/012,195, 2023		2023
Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation DP Kingma, R Gao Advances in neural information processing systems 36, 2023	31*	2023
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	237	2023
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	787	2022
Variational Diffusion Models D Kingma, T Salimans, B Poole, J Ho Advances in neural information processing systems 34, 21696-21707, 2021	759	2021
On Linear Identifiability of Learned Representations G Roeder, L Metz, D Kingma International Conference on Machine Learning, 9030-9039, 2021	66	2021
Wave-tacotron: Spectrogram-free end-to-end text-to-speech synthesis RJ Weiss, RJ Skerry-Ryan, E Battenberg, S Mariooryad, DP Kingma ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021	113	2021
How to train your energy-based models Y Song, DP Kingma arXiv preprint arXiv:2101.03288, 2021	216	2021
Learning energy-based models by diffusion recovery likelihood R Gao, Y Song, B Poole, YN Wu, DP Kingma arXiv preprint arXiv:2012.08125, 2020	107	2020
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	3509	2020
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA I Khemakhem, R Monti, D Kingma, A Hyvarinen Advances in Neural Information Processing Systems 33, 12768-12778, 2020	116*	2020
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	106	2020
Variational Autoencoders and Nonlinear ICA: A Unifying Framework I Khemakhem, DP Kingma, A Hyvärinen The 23rd International Conference on Artificial Intelligence and Statistics …, 2019	541	2019
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	239*	2019
A method for stochastic optimization. arXiv: 14126980 [cs], 2017 DP Kingma, BJ Adam arXiv preprint arXiv:1412.6980, 2019	435	2019
An Introduction to Variational Autoencoders DP Kingma, M Welling Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019	2565	2019
Glow: Generative Flow with Invertible 1x1 Convolutions DP Kingma, P Dhariwal Advances in Neural Information Processing Systems, 10215-10224, 2018	3183	2018
Learning Sparse Neural Networks through Regularization C Louizos, M Welling, DP Kingma Proceedings of the International Conference on Learning Representations (ICLR), 2017	1178	2017
Variational Inference and Deep Learning: A New Synthesis (Ph.D. Thesis) DP Kingma Universiteit van Amsterdam, 2017	83*	2017

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