4.6k citations · 10k across the 22 of their papers we have counts for
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Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo +4
We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep gene…
Improving Sampling from Generative Autoencoders with Markov Chains
Antonia Creswell, Kai Arulkumaran, Anil Anthony Bharath
We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. Generative autoencoders a…
Towards Deep Symbolic Reinforcement Learning
Marta Garnelo, Kai Arulkumaran, Murray Shanahan
Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demo…
Classifying Options for Deep Reinforcement Learning
Kai Arulkumaran, Nat Dilokthanakul, Murray Shanahan +1
In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different "option heads" on the…