P
Padhraic Smyth
Researcher at University of California, Irvine
Publications - 359
Citations - 38795
Padhraic Smyth is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Inference & Topic model. The author has an hindex of 80, co-authored 342 publications receiving 36653 citations. Previous affiliations of Padhraic Smyth include University of California & Jet Propulsion Laboratory.
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Journal ArticleDOI
Content Coding of Psychotherapy Transcripts Using Labeled Topic Models
TL;DR: The labeled latent Dirichlet allocation (L-LDA) model has the potential to be an objective, scalable method for accurate automated coding of psychotherapy sessions that perform better than comparable discriminative methods at session-level coding and can also predict fine-grained codes.
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A Bayesian Hidden Markov Model of Daily Precipitation over South and East Asia
TL;DR: In this article, a Bayesian hidden Markov model (HMM) and a generalized linear model (GLM) are combined with spatiotemporal averages of observed rainfall as a predictor.
Journal ArticleDOI
A Bayesian Mixture Approach to Modeling Spatial Activation Patterns in Multisite fMRI Data
TL;DR: A probabilistic model for analyzing spatial activation patterns in multiple functional magnetic resonance imaging (fMRI) activation images such as repeated observations on an individual or images from different individuals in a clinical study is proposed.
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Asynchronous distributed estimation of topic models for document analysis
TL;DR: A distributed learning framework for Latent Dirichlet Allocation, a well-known Bayesian latent variable model for sparse matrices of count data, is presented and simulations suggest that asynchronous distributed algorithms are able to learn models that are nearly as accurate as those learned by the standard non-distributed approaches.
Proceedings ArticleDOI
Recursive Neural Networks for Coding Therapist and Patient Behavior in Motivational Interviewing
Michael J. Tanana,Kevin A. Hallgren,Zac E. Imel,David C. Atkins,Padhraic Smyth,Vivek Srikumar +5 more
TL;DR: The development and testing of a recursive neural network (RNN) model for rating therapist and patient talk turns across 356 MI sessions are described and the accuracy of RNNs in predicting human ratings for client speech is assessed.