The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning
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.
Cite
Text
Chen et al. "The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning." International Conference on Machine Learning, 2011.Markdown
[Chen et al. "The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning." International Conference on Machine Learning, 2011.](https://mlanthology.org/icml/2011/chen2011icml-hierarchical/)BibTeX
@inproceedings{chen2011icml-hierarchical,
title = {{The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning}},
author = {Chen, Bo and Polatkan, Gungor and Sapiro, Guillermo and Dunson, David B. and Carin, Lawrence},
booktitle = {International Conference on Machine Learning},
year = {2011},
pages = {361-368},
url = {https://mlanthology.org/icml/2011/chen2011icml-hierarchical/}
}