Diederik P. Kingma
Diederik P. Kingma
Research Scientist, Google Brain
Dirección de correo verificada de google.com - Página principal
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Citado por
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Año
Adam: A Method for Stochastic Optimization
DP Kingma, J Ba
Proceedings of the 3rd International Conference on Learning Representations …, 2014
888132014
Auto-Encoding Variational Bayes
DP Kingma, M Welling
Proceedings of the 2nd International Conference on Learning Representations …, 2013
170182013
Semi-Supervised Learning with Deep Generative Models
DP Kingma, S Mohamed, DJ Rezende, M Welling
Advances in Neural Information Processing Systems, 3581-3589, 2014
22642014
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
12922016
Glow: Generative Flow with Invertible 1x1 Convolutions
DP Kingma, P Dhariwal
Advances in Neural Information Processing Systems, 10215-10224, 2018
12452018
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
12232016
Variational Dropout and the Local Reparameterization Trick
DP Kingma, T Salimans, M Welling
Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015
8982015
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
5772017
Learning Sparse Neural Networks through Regularization
C Louizos, M Welling, DP Kingma
Proceedings of the International Conference on Learning Representations (ICLR), 2017
5352017
An Introduction to Variational Autoencoders
DP Kingma, M Welling
Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019
5202019
Variational Lossy Autoencoder
X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ...
arXiv preprint arXiv:1611.02731, 2016
5112016
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
4612014
Stochastic gradient VB and the variational auto-encoder
DP Kingma, M Welling
Second International Conference on Learning Representations, ICLR 19, 121, 2014
2442014
Adam: A method for stochastic optimization. ICLR 2015
DP Kingma, J Ba
arXiv preprint arXiv:1412.6980 9, 2015
228*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
1372019
Adam: A Method for Stochastic Optimization.(2014). arXiv
DP Kingma, J Ba
arXiv preprint arXiv:1412.6980, 2014
122*2014
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
116*2019
GPU Kernels for Block-Sparse Weights
S Gray, A Radford, DP Kingma
arXiv preprint arXiv:1711.09224 3, 2017
1042017
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
722020
Regularized Estimation of Image Statistics by Score Matching
DP Kingma, Y LeCun
Advances in Neural Information Processing Systems 23, 1126-1134, 2010
612010
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Artículos 1–20