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Sean Gerrish
Researcher at Princeton University
Publications - 13
Citations - 3662
Sean Gerrish is an academic researcher from Princeton University. The author has contributed to research in topics: Inference & Voting. The author has an hindex of 11, co-authored 13 publications receiving 3428 citations.
Papers
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Proceedings Article
Reading Tea Leaves: How Humans Interpret Topic Models
TL;DR: New quantitative methods for measuring semantic meaning in inferred topics are presented, showing that they capture aspects of the model that are undetected by previous measures of model quality based on held-out likelihood.
Posted Content
Black Box Variational Inference
TL;DR: The authors proposed a black box variational inference algorithm based on a stochastic optimization of the variational objective, where the noisy gradient is computed from Monte Carlo samples from the Variational distribution, which can be applied to many models with little additional derivation.
Proceedings Article
Black Box Variational Inference
TL;DR: This paper presents a "black box" variational inference algorithm, one that can be quickly applied to many models with little additional derivation, based on a stochastic optimization of the variational objective where the noisy gradient is computed from Monte Carlo samples from the Variational distribution.
Proceedings Article
Predicting Legislative Roll Calls from Text
Sean Gerrish,David M. Blei +1 more
TL;DR: This work develops several predictive models linking legislative sentiment to legislative text and derives approximate posterior inference algorithms based on variational methods that predict specific voting patterns with high accuracy across 12 years of legislative data.
Proceedings Article
A Language-based Approach to Measuring Scholarly Impact
Sean Gerrish,David M. Blei +1 more
TL;DR: This work proposes using changes in the thematic content of documents over time to measure the importance of individual documents within the collection, and describes a dynamic topic model for both quantifying and qualifying the impact of these documents.