208 citations · 963 across the 30 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
Interacting Particle Markov Chain Monte Carlo
Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten +4
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like r…