The hierarchical beta process for convolutional factor analysis and deep learning

Bo Chen, Gungor Polatkan, Guillermo Sapiro, David B. Dunson, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Scopus citations

Abstract

A convolutional factor-analysis model is developed, with the number of filters (factors) inferred via the beta process (BP) and hierarchical BP, for single-task and multi-task learning, respectively. The computation of the model parameters is implemented within a Bayesian setting, employing Gibbs sampling; we explicitly exploit the convolutional nature of the expansion to accelerate computations. The model is used in a multi-level ("deep") analysis of general data, with specific results presented for image-processing data sets, e.g., classification. Copyright 2011 by the author(s)/owner(s).
Original languageEnglish (US)
Title of host publicationProceedings of the 28th International Conference on Machine Learning, ICML 2011
Pages361-368
Number of pages8
StatePublished - Oct 7 2011
Externally publishedYes

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