83.4k citations · 107.7k across the 29 of their papers we have counts for
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma, Jimmy Ba
We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is str…
Variational Recurrent Auto-Encoders
Otto Fabius, Joost R. van Amersfoort
In this paper we propose a model that combines the strengths of RNNs and SGVB: the Variational Recurrent Auto-Encoder (VRAE). Such a model can be used for efficient, large scale un…
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
Tim Salimans, Diederik P. Kingma, Max Welling
Recent advances in stochastic gradient variational inference have made it possible to perform variational Bayesian inference with posterior approximations containing auxiliary rand…
Semi-Supervised Learning with Deep Generative Models
Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed +1
The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practi…
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets
Diederik P. Kingma, Max Welling
Hierarchical Bayesian networks and neural networks with stochastic hidden units are commonly perceived as two separate types of models. We show that either of these types of models…