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|Abstract:||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 inference techniques. We then evaluate various DEFs on text and combine multiple DEFs into a model for pairwise recommendation data. In an extensive study, we show that going beyond one layer improves predictions for DEFs. We demonstrate that DEFs find interesting exploratory structure in large data sets, and give better predictive performance than state-of-the-art models.|
|Citation:||Ranganath, Rajesh, Linpeng Tang, Laurent Charlin, and David Blei. "Deep Exponential Families." Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics 38 (2015): pp. 762-771.|
|Pages:||762 - 771|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
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