The hierarchical beta process for convolutional factor analysis and deep learning

Journal Article

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).

Duke Authors

Cited Authors

  • Chen, B; Polatkan, G; Sapiro, G; Dunson, DB; Carin, L

Published Date

  • October 7, 2011

Published In

  • Proceedings of the 28th International Conference on Machine Learning, Icml 2011

Start / End Page

  • 361 - 368

Citation Source

  • Scopus