activity
20152021
most citedLearning Disentangled Representations with Semi-Supervised Deep Generative Models

140 citations · 176 across the 5 of their papers we have counts for

collaborators

20 papers

cs.CL2021

On the Impact of Random Seeds on the Fairness of Clinical Classifiers

Silvio Amir, Jan-Willem van de Meent, Byron C. Wallace

Recent work has shown that fine-tuning large networks is surprisingly sensitive to changes in random seed(s). We explore the implications of this phenomenon for model fairness acro…

cs.CL2021

Disentangling Representations of Text by Masking Transformers

Xiongyi Zhang, Jan-Willem van de Meent, Byron C. Wallace

Representations from large pretrained models such as BERT encode a range of features into monolithic vectors, affording strong predictive accuracy across a multitude of downstream…

stat.ML2021

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…

cs.RO2021

Action Priors for Large Action Spaces in Robotics

Ondrej Biza, Dian Wang, Robert Platt +2

In robotics, it is often not possible to learn useful policies using pure model-free reinforcement learning without significant reward shaping or curriculum learning. As a conseque…

cs.LG2020

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

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…