Diederik P. Kingma

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Tim SalimansGoogle DeepMind AmsterdamPotvrđena adresa e-pošte na google.com
Jimmy BaUniversity of TorontoPotvrđena adresa e-pošte na cs.toronto.edu
Max WellingCAIO CuspAI & Professor Machine Learning, University of AmsterdamPotvrđena adresa e-pošte na uva.nl
Prafulla DhariwalResearcher, OpenAIPotvrđena adresa e-pošte na openai.com
Ben PooleResearch Scientist at Google DeepMindPotvrđena adresa e-pošte na stanford.edu
Jascha Sohl-DicksteinAnthropicPotvrđena adresa e-pošte na anthropic.com
RUIQI GAOResearch Scientist, Google DeepMindPotvrđena adresa e-pošte na google.com
(Peter) Xi Chencovariant.ai | UC BerkeleyPotvrđena adresa e-pošte na berkeley.edu
Jonathan HoPotvrđena adresa e-pošte na berkeley.edu
Yang SongOpenAIPotvrđena adresa e-pošte na openai.com
Ilya SutskeverCo-Founder and Chief Scientist of OpenAIPotvrđena adresa e-pošte na openai.com
Stefano ErmonStanford UniversityPotvrđena adresa e-pošte na cs.stanford.edu
Abhishek KumarGoogle BrainPotvrđena adresa e-pošte na google.com
David J FleetGoogle DeepMind; University of Toronto; Vector InstitutePotvrđena adresa e-pošte na cs.toronto.edu
Shakir MohamedResearch Director, Google DeepMindPotvrđena adresa e-pošte na deepmind.com
Danilo J. RezendeDirector at DeepMindPotvrđena adresa e-pošte na google.com
Aapo HyvarinenUniversity of HelsinkiPotvrđena adresa e-pošte na helsinki.fi
Ilyes KhemakhemPhD candidate, Gatsby Unit, UCLPotvrđena adresa e-pošte na gatsby.ucl.ac.uk
Ricardo Pio MontiGatsby Unit, UCLPotvrđena adresa e-pošte na gatsby.ucl.ac.uk
Alexey A. GritsenkoGoogle DeepMindPotvrđena adresa e-pošte na google.com

Prati

Diederik P. Kingma

Ostala imenaDurk Kingma, Diederik Pieter Kingma

Research Scientist, Google Brain

Potvrđena adresa e-pošte na google.com - Početna stranica

Machine Learning Deep Learning Neural Networks Generative Models Artificial Intelligence


Naslov Poredaj po navodima Poredaj po godini Poredaj po naslovu	Citirano Citirano	Godina
Adam: A method for stochastic optimization DP Kingma, J Ba arXiv preprint arXiv:1412.6980, 2014	194156	2014
Auto-Encoding Variational Bayes DP Kingma, M Welling arXiv preprint arXiv:1312.6114, 2013	38670	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	4290	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	3601	2014
Glow: Generative Flow with Invertible 1x1 Convolutions DP Kingma, P Dhariwal Advances in Neural Information Processing Systems, 10215-10224, 2018	3368	2018
An Introduction to Variational Autoencoders DP Kingma, M Welling Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019	2869	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	2288	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	2121	2016
Variational Dropout and the Local Reparameterization Trick DP Kingma, T Salimans, M Welling Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015	1745	2015
Learning Sparse Neural Networks through Regularization C Louizos, M Welling, DP Kingma Proceedings of the International Conference on Learning Representations (ICLR), 2017	1244	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	1139	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	1024	2022
Variational Diffusion Models D Kingma, T Salimans, B Poole, J Ho Advances in neural information processing systems 34, 21696-21707, 2021	877	2021
Variational Lossy Autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016	806	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	719	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	622*	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	585	2019
a method for stochastic optimization. arXiv 1412.6980. 2014 DP Kingma, BJ Adam E0000, 2018	397	2018
Adam: A method for stochastic optimization 3rd International Conference on Learning Representations DP Kingma, JL Ba ICLR 2015-Conference Track Proceedings 1, 2015	374	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	284	2023

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