The hierarchical beta process for convolutional factor analysis and deep learning

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

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 proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Proceedings of the 28th International Conference on Machine Learning, ICML 2011
Pages	361-368
Number of pages	8
State	Published - 2011
Externally published	Yes
Event	28th International Conference on Machine Learning, ICML 2011 - Bellevue, WA, United States Duration: Jun 28 2011 → Jul 2 2011

Publication series

Name	Proceedings of the 28th International Conference on Machine Learning, ICML 2011

Other

Other	28th International Conference on Machine Learning, ICML 2011
Country/Territory	United States
City	Bellevue, WA
Period	6/28/11 → 7/2/11

All Science Journal Classification (ASJC) codes

Computer Science Applications
Human-Computer Interaction
Education

Cite this

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title = "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.",

author = "Bo Chen and Gungor Polatkan and Guillermo Sapiro and Dunson, {David B.} and Lawrence Carin",

year = "2011",

language = "English (US)",

isbn = "9781450306195",

series = "Proceedings of the 28th International Conference on Machine Learning, ICML 2011",

pages = "361--368",

booktitle = "Proceedings of the 28th International Conference on Machine Learning, ICML 2011",

note = "28th International Conference on Machine Learning, ICML 2011 ; Conference date: 28-06-2011 Through 02-07-2011",

}

Chen, B, Polatkan, G, Sapiro, G, Dunson, DB & Carin, L 2011, The hierarchical beta process for convolutional factor analysis and deep learning. in Proceedings of the 28th International Conference on Machine Learning, ICML 2011. Proceedings of the 28th International Conference on Machine Learning, ICML 2011, pp. 361-368, 28th International Conference on Machine Learning, ICML 2011, Bellevue, WA, United States, 6/28/11.

The hierarchical beta process for convolutional factor analysis and deep learning. / Chen, Bo; Polatkan, Gungor; Sapiro, Guillermo et al.
Proceedings of the 28th International Conference on Machine Learning, ICML 2011. 2011. p. 361-368 (Proceedings of the 28th International Conference on Machine Learning, ICML 2011).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Polatkan, Gungor

AU - Sapiro, Guillermo

AU - Dunson, David B.

AU - Carin, Lawrence

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:80053437590

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T3 - Proceedings of the 28th International Conference on Machine Learning, ICML 2011

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BT - Proceedings of the 28th International Conference on Machine Learning, ICML 2011

T2 - 28th International Conference on Machine Learning, ICML 2011

Y2 - 28 June 2011 through 2 July 2011

ER -

The hierarchical beta process for convolutional factor analysis and deep learning

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

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