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Publications [#265128] of Guillermo Sapiro

Papers Published

  1. 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 (October, 2011), pp. 361-368
    (last updated on 2019/06/25)

    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).
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Mathematics Department
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