140 citations · 411 across the 24 of their papers we have counts for
3 papers · 1 filter
Variational Bayesian Optimal Experimental Design
Adam Foster, Martin Jankowiak, Eli Bingham +4
Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by th…
Tensor Variable Elimination for Plated Factor Graphs
Fritz Obermeyer, Eli Bingham, Martin Jankowiak +4
A wide class of machine learning algorithms can be reduced to variable elimination on factor graphs. While factor graphs provide a unifying notation for these algorithms, they do n…
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
N. Siddharth, Brooks Paige, Jan-Willem van de Meent +5
Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the dat…