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20142021
most citedLearning Disentangled Representations with Semi-Supervised Deep Generative Models

140 citations · 341 across the 21 of their papers we have counts for

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5 papers · 1 filter

cs.LG2020★ 4 cited

Query-Focused EHR Summarization to Aid Imaging Diagnosis

Denis Jered McInerney, Borna Dabiri, Anne-Sophie Touret +3

Electronic Health Records (EHRs) provide vital contextual information to radiologists and other physicians when making a diagnosis. Unfortunately, because a given patient's record…

cs.LG2020★ 2 cited

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…

cs.LG2020★ 6 cited

Learning Discrete State Abstractions With Deep Variational Inference

Ondrej Biza, Robert Platt, Jan-Willem van de Meent +1

Abstraction is crucial for effective sequential decision making in domains with large state spaces. In this work, we propose an information bottleneck method for learning approxima…

cs.LG2019★ 4 cited

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…

cs.LG2018★ 2 cited

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…