7 citations · 21 across the 6 of their papers we have counts for
5 papers · 1 filter
Semi-supervised Sequential Generative Models
Michael Teng, Tuan Anh Le, Adam Scibior +1
We introduce a novel objective for training deep generative time-series models with discrete latent variables for which supervision is only sparsely available. This instance of sem…
The Thermodynamic Variational Objective
Vaden Masrani, Tuan Anh Le, Frank Wood
We introduce the thermodynamic variational objective (TVO) for learning in both continuous and discrete deep generative models. The TVO arises from a key connection between variati…
Imitation Learning of Factored Multi-agent Reactive Models
Michael Teng, Tuan Anh Le, Adam Scibior +1
We apply recent advances in deep generative modeling to the task of imitation learning from biological agents. Specifically, we apply variations of the variational recurrent neural…
Deep Variational Reinforcement Learning for POMDPs
Maximilian Igl, Luisa Zintgraf, Tuan Anh Le +2
Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinfo…
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning
Tuan Anh Le, Atilim Gunes Baydin, Robert Zinkov +1
We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning. In particular, training a…