6 citations · 19 across the 8 of their papers we have counts for
8 papers
Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm
Eli Sennesh, Hao Wu, Tommaso Salvatori
Unexpected stimuli induce "error" or "surprise" signals in the brain. The theory of predictive coding promises to explain these observations in terms of Bayesian inference by sugge…
String Diagrams with Factorized Densities
Eli Sennesh, Jan-Willem van de Meent
A growing body of research on probabilistic programs and causal models has highlighted the need to reason compositionally about model classes that extend directed graphical models.…
Deriving time-averaged active inference from control principles
Eli Sennesh, Jordan Theriault, Jan-Willem van de Meent +2
Active inference offers a principled account of behavior as minimizing average sensory surprise over time. Applications of active inference to control problems have heretofore tend…
Learning Proposals for Probabilistic Programs with Inference Combinators
Sam Stites, Heiko Zimmermann, Hao Wu +2
We develop operators for construction of proposals in probabilistic programs, which we refer to as inference combinators. Inference combinators define a grammar over importance sam…
Deep Markov Spatio-Temporal Factorization
Amirreza Farnoosh, Behnaz Rezaei, Eli Zachary Sennesh +6
We introduce deep Markov spatio-temporal factorization (DMSTF), a generative model for dynamical analysis of spatio-temporal data. Like other factor analysis methods, DMSTF approxi…
Amortized Population Gibbs Samplers with Neural Sufficient Statistics
Hao Wu, Heiko Zimmermann, Eli Sennesh +2
We develop amortized population Gibbs (APG) samplers, a class of scalable methods that frames structured variational inference as adaptive importance sampling. APG samplers constru…
Neural Topographic Factor Analysis for fMRI Data
Eli Sennesh, Zulqarnain Khan, Yiyu Wang +4
Neuroimaging studies produce gigabytes of spatio-temporal data for a small number of participants and stimuli. Rarely do researchers attempt to model and examine how individual par…
Composing Modeling and Inference Operations with Probabilistic Program Combinators
Eli Sennesh, Adam Ścibior, Hao Wu +1
Probabilistic programs with dynamic computation graphs can define measures over sample spaces with unbounded dimensionality, which constitute programmatic analogues to Bayesian non…