TY - GEN
T1 - The hierarchical beta process for convolutional factor analysis and deep learning
AU - Chen, Bo
AU - Polatkan, Gungor
AU - Sapiro, Guillermo
AU - Dunson, David B.
AU - Carin, Lawrence
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2011/10/7
Y1 - 2011/10/7
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. Copyright 2011 by the author(s)/owner(s).
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. Copyright 2011 by the author(s)/owner(s).
UR - http://www.scopus.com/inward/record.url?scp=80053437590&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9781450306195
SP - 361
EP - 368
BT - Proceedings of the 28th International Conference on Machine Learning, ICML 2011
ER -