Diederik P. Kingma
Diederik P. Kingma
Research Scientist, OpenAI, San Francisco
Geverifieerd e-mailadres voor openai.com - Homepage
TitelGeciteerd doorJaar
Adam: A Method for Stochastic Optimization
DP Kingma, J Ba
Proceedings of the 3rd International Conference on Learning Representations …, 2014
103862014
Auto-Encoding Variational Bayes
DP Kingma, M Welling
Proceedings of the 2nd International Conference on Learning Representations …, 2013
24702013
Semi-Supervised Learning with Deep Generative Models
DP Kingma, S Mohamed, DJ Rezende, M Welling
Advances in Neural Information Processing Systems, 3581-3589, 2014
5462014
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
2152016
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
1872016
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
1622014
Variational Dropout and the Local Reparameterization Trick
DP Kingma, T Salimans, M Welling
Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015
1482015
Variational lossy autoencoder
X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ...
arXiv preprint arXiv:1611.02731, 2016
792016
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
612017
Regularized Estimation of Image Statistics by Score Matching
DP Kingma, Y LeCun
Advances in Neural Information Processing Systems 23, 1126-1134, 2010
322010
a method for stochastic optimization. 2014
DP Kingma, BJ Adam
arXiv preprint arXiv:1412.6980, 2015
282015
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
232014
Fast Gradient-based Inference With Continuous Latent Variable Models in Auxiliary Form
DP Kingma
arXiv preprint arXiv:1306.0733, 2013
122013
A method for stochastic optimization [J]. arXiv preprint
D Kingma, BJ Adam
arXiv preprint arXiv:1412.6980, 2014
102014
Learning Sparse Neural Networks through Regularization
C Louizos, M Welling, DP Kingma
arXiv preprint arXiv:1712.01312, 2017
72017
Variational Inference and Deep Learning: A New Synthesis (Ph.D. Thesis)
DP Kingma
Universiteit van Amsterdam, 2017
5*2017
Auto-encoding variational bayes [J]
DP Kingma, M Welling
42013
GPU kernels for block-sparse weights
S Gray, A Radford, DP Kingma
Technical report, OpenAI, 2017
32017
Our first research results are now live: four projects that share a common theme of enhancing or using generative models, a branch of unsupervised learning techniq...
A Karpathy, P Abbeel, G Brockman
22016
Note on Equivalence Between Recurrent Neural Network Time Series Models and Variational Bayesian Models
J Sohl-Dickstein, DP Kingma
arXiv preprint arXiv:1504.08025, 2015
22015
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