21 citations · 34 across the 4 of their papers we have counts for
4 papers
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…
Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs
David Tolpin, Jan Willem van de Meent, Brooks Paige +1
We introduce an adaptive output-sensitive Metropolis-Hastings algorithm for probabilistic models expressed as programs, Adaptive Lightweight Metropolis-Hastings (AdLMH). The algori…
Asynchronous Anytime Sequential Monte Carlo
Brooks Paige, Frank Wood, Arnaud Doucet +1
We introduce a new sequential Monte Carlo algorithm we call the particle cascade. The particle cascade is an asynchronous, anytime alternative to traditional particle filtering alg…