Diederik P. Kingma
Diederik P. Kingma
Research Scientist, Google Brain
Adresse e-mail validée de google.com - Page d'accueil
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Adam: A Method for Stochastic Optimization
DP Kingma, J Ba
Proceedings of the 3rd International Conference on Learning Representations …, 2014
817682014
Auto-Encoding Variational Bayes
DP Kingma, M Welling
Proceedings of the 2nd International Conference on Learning Representations …, 2013
157402013
Semi-Supervised Learning with Deep Generative Models
DP Kingma, S Mohamed, DJ Rezende, M Welling
Advances in Neural Information Processing Systems, 3581-3589, 2014
21412014
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
12212016
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
11382016
Glow: Generative Flow with Invertible 1x1 Convolutions
DP Kingma, P Dhariwal
Advances in Neural Information Processing Systems, 10215-10224, 2018
11232018
Variational Dropout and the Local Reparameterization Trick
DP Kingma, T Salimans, M Welling
Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015
8302015
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
5412017
Variational Lossy Autoencoder
X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ...
arXiv preprint arXiv:1611.02731, 2016
4812016
Learning Sparse Neural Networks through Regularization
C Louizos, M Welling, DP Kingma
Proceedings of the International Conference on Learning Representations (ICLR), 2017
4642017
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
4502014
An Introduction to Variational Autoencoders
DP Kingma, M Welling
Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019
4172019
Ba J.: Adam: A method for stochastic optimization
DP Kingma
arXiv preprint arXiv:1412.6980 531, 2014
2642014
The 3rd International Conference for Learning Representations
DP Kingma, J Ba
ICLR, San Diego, 2015
1332015
Auto-encoding variational bayes. arXiv
DP Kingma, M Welling
arXiv preprint arXiv:1312.6114, 2013
1242013
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
I Khemakhem, DP Kingma, A Hyvärinen
The 23rd International Conference on Artificial Intelligence and Statistics …, 2019
1212019
A method for stochastic optimization. arXiv preprint 2014
DP Kingma, BJ Adam
arXiv preprint arXiv:1412.6980 205, 2014
1172014
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
108*2019
GPU Kernels for Block-Sparse Weights
S Gray, A Radford, DP Kingma
arXiv preprint arXiv:1711.09224 3, 2017
932017
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets
DP Kingma, M Welling
Proceedings of the International Conference on Machine Learning (ICML), 2014
582014
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