140 citations · 735 across the 46 of their papers we have counts for
Showing 2016 · stat.MLShow all
3 papers · 2 filters
stat.ML2016★ 7 cited
Inducing Interpretable Representations with Variational Autoencoders
N. Siddharth, Brooks Paige, Alban Desmaison +5
We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representation…
stat.ML2016★ 5 cited
Probabilistic structure discovery in time series data
David Janz, Brooks Paige, Tom Rainforth +2
Existing methods for structure discovery in time series data construct interpretable, compositional kernels for Gaussian process regression models. While the learned Gaussian proce…
stat.ML2016★ 48 cited
Inference Networks for Sequential Monte Carlo in Graphical Models
Brooks Paige, Frank Wood
We introduce a new approach for amortizing inference in directed graphical models by learning heuristic approximations to stochastic inverses, designed specifically for use as prop…