Math @ Duke

Publications [#265128] of Guillermo Sapiro
Papers Published
 Chen, B; Polatkan, G; Sapiro, G; Dunson, DB; Carin, L, The hierarchical beta process for convolutional factor analysis and deep learning,
Proceedings of the 28th International Conference on Machine Learning, ICML 2011
(2011),
pp. 361368
(last updated on 2018/05/28)
Abstract: A convolutional factoranalysis model is developed, with the number of filters (factors) inferred via the beta process (BP) and hierarchical BP, for singletask and multitask 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 multilevel ("deep") analysis of general data, with specific results presented for imageprocessing data sets, e.g., classification. Copyright 2011 by the author(s)/owner(s).


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