Seuraa
Diederik P. Kingma
Diederik P. Kingma
Research Scientist, Google Brain
Vahvistettu sähköpostiosoite verkkotunnuksessa google.com - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
Adam: A Method for Stochastic Optimization
DP Kingma, J Ba
Proceedings of the 3rd International Conference on Learning Representations …, 2014
1091002014
Auto-Encoding Variational Bayes
DP Kingma, M Welling
arXiv preprint arXiv:1312.6114, 2013
207552013
Semi-Supervised Learning with Deep Generative Models
DP Kingma, S Mohamed, DJ Rezende, M Welling
Advances in Neural Information Processing Systems, 3581-3589, 2014
25222014
Glow: Generative Flow with Invertible 1x1 Convolutions
DP Kingma, P Dhariwal
Advances in Neural Information Processing Systems, 10215-10224, 2018
16362018
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
14712016
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
14062016
Variational Dropout and the Local Reparameterization Trick
DP Kingma, T Salimans, M Welling
Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015
10782015
An Introduction to Variational Autoencoders
DP Kingma, M Welling
Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019
8452019
Adam: A Method for Stochastic Optimization.(2014). arXiv
DP Kingma, J Ba
arXiv preprint arXiv:1412.6980, 2014
802*2014
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
6872017
Learning Sparse Neural Networks through Regularization
C Louizos, M Welling, DP Kingma
Proceedings of the International Conference on Learning Representations (ICLR), 2017
6682017
Variational Lossy Autoencoder
X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ...
arXiv preprint arXiv:1611.02731, 2016
5812016
Adam: A method for stochastic optimization preprint
DP Kingma, J Ba
arXiv preprint arXiv:1412.6980, 2014
517*2014
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
5152014
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
I Khemakhem, DP Kingma, A Hyvärinen
The 23rd International Conference on Artificial Intelligence and Statistics …, 2019
1952019
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
1852020
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
152*2019
GPU Kernels for Block-Sparse Weights
S Gray, A Radford, DP Kingma
arXiv preprint arXiv:1711.09224 3, 2017
1242017
Adam: A method for stochastic optimization in: Proceedings of the 3rd international conference for learning representations (iclr’15)
D Kingma, J Ba
San Diego, 2015
752015
Adam: A method for stochastic optimization, ILCR
DP Kingma, BJ Lei
71*2014
Järjestelmä ei voi suorittaa toimenpidettä nyt. Yritä myöhemmin uudelleen.
Artikkelit 1–20