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Diederik P. Kingma
Diederik P. Kingma
Sonstige NamenDurk Kingma, Diederik Pieter Kingma
Research Scientist, Google Brain
Bestätigte E-Mail-Adresse bei google.com - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Adam: A method for stochastic optimization
DP Kingma, J Ba
arXiv preprint arXiv:1412.6980, 2014
1896612014
Auto-Encoding Variational Bayes
DP Kingma, M Welling
arXiv preprint arXiv:1312.6114, 2013
373702013
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
39402020
Semi-Supervised Learning with Deep Generative Models
DP Kingma, S Mohamed, DJ Rezende, M Welling
Advances in Neural Information Processing Systems, 3581-3589, 2014
35272014
Glow: Generative Flow with Invertible 1x1 Convolutions
DP Kingma, P Dhariwal
Advances in Neural Information Processing Systems, 10215-10224, 2018
32642018
An Introduction to Variational Autoencoders
DP Kingma, M Welling
Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019
27152019
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
22352016
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
20822016
Variational Dropout and the Local Reparameterization Trick
DP Kingma, T Salimans, M Welling
Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015
17052015
Learning Sparse Neural Networks through Regularization
C Louizos, M Welling, DP Kingma
Proceedings of the International Conference on Learning Representations (ICLR), 2017
12182017
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
11102017
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
9342022
Variational Diffusion Models
D Kingma, T Salimans, B Poole, J Ho
Advances in neural information processing systems 34, 21696-21707, 2021
8282021
Variational Lossy Autoencoder
X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ...
arXiv preprint arXiv:1611.02731, 2016
7932016
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
7102014
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
609*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
5672019
Adam: A method for stochastic optimization 3rd International Conference on Learning Representations
DP Kingma, JL Ba
ICLR 2015-Conference Track Proceedings 1, 2015
3512015
Adam: a method for stochastic optimization. arXiv e-prints
DP Kingma, J Ba
arXiv preprint arXiv:1412.6980 1412, 2014
2732014
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
2692023
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