D
Diederik P. Kingma
Researcher at Google
Publications - 45
Citations - 173232
Diederik P. Kingma is an academic researcher from Google. The author has contributed to research in topics: Inference & Artificial neural network. The author has an hindex of 27, co-authored 42 publications receiving 130871 citations. Previous affiliations of Diederik P. Kingma include OpenAI & University of Amsterdam.
Papers
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
Proceedings Article
Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.
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Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
TL;DR: In this paper, a stochastic variational inference and learning algorithm was proposed for directed probabilistic models with intractable posterior distributions and large datasets, which scales to large datasets.
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Semi-Supervised Learning with Deep Generative Models
TL;DR: It is shown that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning.