2 citations · 5 across the 5 of their papers we have counts for
5 papers
Exploration with Multi-Sample Target Values for Distributional Reinforcement Learning
Michael Teng, Michiel van de Panne, Frank Wood
Distributional reinforcement learning (RL) aims to learn a value-network that predicts the full distribution of the returns for a given state, often modeled via a quantile-based cr…
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…
Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training
William Harvey, Michael Teng, Frank Wood
Hard visual attention is a promising approach to reduce the computational burden of modern computer vision methodologies. Hard attention mechanisms are typically non-differentiable…
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…
High Throughput Synchronous Distributed Stochastic Gradient Descent
Michael Teng, Frank Wood
We introduce a new, high-throughput, synchronous, distributed, data-parallel, stochastic-gradient-descent learning algorithm. This algorithm uses amortized inference in a compute-c…