Diederik P. Kingma

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Jimmy BaUniversity of TorontoPatvirtintas el. paštas cs.toronto.edu
Max WellingCAIO CuspAI & Professor Machine Learning, University of AmsterdamPatvirtintas el. paštas uva.nl
Tim SalimansGoogle DeepMind AmsterdamPatvirtintas el. paštas google.com
Prafulla DhariwalResearcher, OpenAIPatvirtintas el. paštas openai.com
Ben PooleResearch Scientist at Google DeepMindPatvirtintas el. paštas stanford.edu
Stefano ErmonStanford UniversityPatvirtintas el. paštas cs.stanford.edu
Yang SongOpenAIPatvirtintas el. paštas openai.com
Jascha Sohl-DicksteinAnthropicPatvirtintas el. paštas anthropic.com
(Peter) Xi Chencovariant.ai | UC BerkeleyPatvirtintas el. paštas berkeley.edu
RUIQI GAOResearch Scientist, Google DeepMindPatvirtintas el. paštas google.com
Ilya SutskeverCo-Founder and Chief Scientist at Safe Superintelligence IncPatvirtintas el. paštas ssi.inc
Jonathan HoPatvirtintas el. paštas berkeley.edu
Abhishek KumarGoogle BrainPatvirtintas el. paštas google.com
Shakir MohamedResearch Director, Google DeepMindPatvirtintas el. paštas deepmind.com
Danilo Jimenez RezendeEllison Institute of TechnologyPatvirtintas el. paštas eit.org
Yann LeCunChief AI Scientist at Facebook & JT Schwarz Professor at the Courant Institute, New York UniversityPatvirtintas el. paštas cs.nyu.edu
Aapo HyvärinenUniversity of HelsinkiPatvirtintas el. paštas helsinki.fi
Ilyes KhemakhemPhD candidate, Gatsby Unit, UCLPatvirtintas el. paštas gatsby.ucl.ac.uk
Ricardo Pio MontiGatsby Unit, UCLPatvirtintas el. paštas gatsby.ucl.ac.uk
Christos LouizosQualcomm AI ResearchPatvirtintas el. paštas qti.qualcomm.com

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Diederik P. Kingma

Kiti vardaiDurk Kingma, Diederik Pieter Kingma

Anthropic

Patvirtintas el. paštas anthropic.com - Pagrindinis puslapis

Machine Learning Deep Learning Neural Networks Generative Models Artificial Intelligence


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Adam: A method for stochastic optimization DP Kingma, J Ba arXiv preprint arXiv:1412.6980, 2014	212205	2014
Auto-Encoding Variational Bayes DP Kingma, M Welling arXiv preprint arXiv:1312.6114, 2013	43649	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	6155	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	3815	2014
Glow: Generative Flow with Invertible 1x1 Convolutions DP Kingma, P Dhariwal Advances in Neural Information Processing Systems, 10215-10224, 2018	3741	2018
An Introduction to Variational Autoencoders DP Kingma, M Welling Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019	3428	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	2419	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	2279	2016
Variational Dropout and the Local Reparameterization Trick DP Kingma, T Salimans, M Welling Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015	1877	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	1390*	2015
Learning Sparse Neural Networks through Regularization C Louizos, M Welling, DP Kingma Proceedings of the International Conference on Learning Representations (ICLR), 2017	1372	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	1232	2017
Variational Diffusion Models D Kingma, T Salimans, B Poole, J Ho Advances in neural information processing systems 34, 21696-21707, 2021	1130	2021
Variational Lossy Autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016	852	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	753	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	680	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	470	2023
Adam: a method for stochastic optimization. arXiv e-prints DP Kingma, J Ba arXiv preprint arXiv:1412.6980 1412, 2014	407	2014
How to train your energy-based models Y Song, DP Kingma arXiv preprint arXiv:2101.03288, 2021	305	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	264*	2019

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