Diederik P. Kingma
Diederik P. Kingma
Research Scientist, Google Brain
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Adam: A Method for Stochastic Optimization
DP Kingma, J Ba
Proceedings of the 3rd International Conference on Learning Representations …, 2014
449952014
Auto-Encoding Variational Bayes
DP Kingma, M Welling
Proceedings of the 2nd International Conference on Learning Representations …, 2013
88792013
Semi-Supervised Learning with Deep Generative Models
DP Kingma, S Mohamed, DJ Rezende, M Welling
Advances in Neural Information Processing Systems, 3581-3589, 2014
14372014
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
7482016
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
6912016
Variational Dropout and the Local Reparameterization Trick
DP Kingma, T Salimans, M Welling
Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015
4892015
Glow: Generative Flow with Invertible 1x1 Convolutions
DP Kingma, P Dhariwal
Advances in Neural Information Processing Systems, 10215-10224, 2018
4612018
Variational Lossy Autoencoder
X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ...
arXiv preprint arXiv:1611.02731, 2016
3142016
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
3062014
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
3032017
Learning Sparse Neural Networks through Regularization
C Louizos, M Welling, DP Kingma
Proceedings of the International Conference on Learning Representations (ICLR), 2017
2042017
Stochastic Gradient VB and the Variational Auto-Encoder
DP Kingma, M Welling
Second International Conference on Learning Representations, ICLR, 2014
1402014
Adam: A method for stochastic gradient descent
DP Kingma, JL Ba
ICLR: International Conference on Learning Representations, 2015
502015
A Method for Stochastic Optimization. arXiv e-prints, page
DP Kingma, JB Adam
arXiv preprint arXiv:1412.6980, 2014
502014
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
482014
Adam: a method for stochastic optimization. CoRR
DP Kingma, J Ba
arXiv preprint arXiv:1412.6980, 2014
472014
GPU Kernels for Block-Sparse Weights
S Gray, A Radford, DP Kingma
arXiv preprint arXiv:1711.09224 3, 2017
462017
Regularized Estimation of Image Statistics by Score Matching
DP Kingma, Y LeCun
Advances in Neural Information Processing Systems 23, 1126-1134, 2010
442010
An Introduction to Variational Autoencoders
DP Kingma, M Welling
Foundations and TrendsŪ in Machine Learning 12 (4), 307-392, 2019
40*2019
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
342019
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Articles 1–20