398 citations · 488 across the 14 of their papers we have counts for
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cs.LG2016★ 56 cited
Generative Multi-Adversarial Networks
Ishan Durugkar, Ian Gemp, Sridhar Mahadevan
Generative adversarial networks (GANs) are a framework for producing a generative model by way of a two-player minimax game. In this paper, we propose the \emph{Generative Multi-Ad…
cs.LG2016★ 2 cited
Inverting Variational Autoencoders for Improved Generative Accuracy
Ian Gemp, Ishan Durugkar, Mario Parente +2
Recent advances in semi-supervised learning with deep generative models have shown promise in generalizing from small labeled datasets () to large unlabeled…
cs.LG2016★ 10 cited
Deep Reinforcement Learning With Macro-Actions
Ishan P. Durugkar, Clemens Rosenbaum, Stefan Dernbach +1
Deep reinforcement learning has been shown to be a powerful framework for learning policies from complex high-dimensional sensory inputs to actions in complex tasks, such as the At…