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- Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng
- ICML
- 2009

There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks. Scaling such models to full-sized, high-dimensional images remains a difficult problem. To address this problem, we present the <i>convolutional deep belief network</i>, a hierarchical generative model which scales to realistic image sizes.… (More)

- Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng
- Commun. ACM
- 2011

There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks (DBNs); however, scaling such models to full-sized, high-dimensional images remains a difficult problem. To address this problem, we present the <i>convolutional deep belief network</i>, a hierarchical generative model that scales to… (More)

- Rajesh Ranganath, Sean Gerrish, David M. Blei
- AISTATS
- 2014

Variational inference has become a widely used method to approximate posteriors in complex latent variables models. However, deriving a variational inference algorithm generally requires significant model-specific analysis. These efforts can hinder and deter us from quickly developing and exploring a variety of models for a problem at hand. In this paper,… (More)

- Daniel Jurafsky, Rajesh Ranganath, Daniel A. McFarland
- HLT-NAACL
- 2009

Automatically extracting social meaning and intention from spoken dialogue is an important task for dialogue and information extraction applications. We describe a system for solving the new task of detecting elements of interactional style: whether a speaker is awkward, friendly, or flirtatious. We create and use a new spoken corpus of approximately 1000… (More)

- Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei
- RecSys
- 2015

Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically, the latent factors are assumed to be static and, given these factors, the observed pref- erences and behaviors of users are assumed to be generated without order. These… (More)

- Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei
- AISTATS
- 2015

We describe deep exponential families (DEFs), a class of latent variable models that are inspired by the hidden structures used in deep neural networks. DEFs capture a hierarchy of dependencies between latent variables, and are easily generalized to many settings through exponential families. We perform inference using recent “black box” variational… (More)

- Prem Gopalan, Francisco J. R. Ruiz, Rajesh Ranganath, David M. Blei
- AISTATS
- 2014

We develop a Bayesian nonparametric Poisson factorization model for recommendation systems. Poisson factorization implicitly models each user’s limited budget of attention (or money) that allows consumption of only a small subset of the available items. In our Bayesian nonparametric variant, the number of latent components is theoretically unbounded and… (More)

- Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David M. Blei
- NIPS
- 2016

Variational inference is an umbrella term for algorithms which cast Bayesian inference as optimization. Classically, variational inference uses the Kullback-Leibler divergence to define the optimization. Though this divergence has been widely used, the resultant posterior approximation can suffer from undesirable statistical properties. To address this, we… (More)

- Rajesh Ranganath, Daniel Jurafsky, Daniel A. McFarland
- EMNLP
- 2009

Automatically detecting human social intentions from spoken conversation is an important task for dialogue understanding. Since the social intentions of the speaker may differ from what is perceived by the hearer, systems that analyze human conversations need to be able to extract both the perceived and the intended social meaning. We investigate this… (More)

- Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David M. Blei
- NIPS
- 2015

Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes it di cult for non-experts to use. We propose an automatic variational inference algorithm, automatic di erentiation variational inference ( ); we implement it in Stan (code… (More)