R
Rajesh Ranganath
Researcher at New York University
Publications - 132
Citations - 9097
Rajesh Ranganath is an academic researcher from New York University. The author has contributed to research in topics: Inference & Computer science. The author has an hindex of 34, co-authored 116 publications receiving 7927 citations. Previous affiliations of Rajesh Ranganath include Stanford University & Courant Institute of Mathematical Sciences.
Papers
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Proceedings ArticleDOI
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
TL;DR: The convolutional deep belief network is presented, a hierarchical generative model which scales to realistic image sizes and is translation-invariant and supports efficient bottom-up and top-down probabilistic inference.
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.
Posted Content
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
TL;DR: ClinicalBERT uncovers high-quality relationships between medical concepts as judged by humans and outperforms baselines on 30-day hospital readmission prediction using both discharge summaries and the first few days of notes in the intensive care unit.
Journal ArticleDOI
Unsupervised learning of hierarchical representations with convolutional deep belief networks
TL;DR: The convolutional deep belief network is presented, a hierarchical generative model that scales to realistic image sizes and is translation-invariant and supports efficient bottom-up and top-down probabilistic inference.