Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke





.......................

.......................


Publications [#264736] of Guillermo Sapiro

Papers Published

  1. Chen, B; Polatkan, G; Sapiro, G; Blei, D; Dunson, D; Carin, L, Deep learning with hierarchical convolutional factor analysis., IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35 no. 8 (August, 2013), pp. 1887-1901 [23787342], [doi]
    (last updated on 2017/12/12)

    Abstract:
    Unsupervised multilayered (“deep”) models are considered for imagery. The model is represented using a hierarchical convolutional factor-analysis construction, with sparse factor loadings and scores. The computation of layer-dependent model parameters is implemented within a Bayesian setting, employing a Gibbs sampler and variational Bayesian (VB) analysis that explicitly exploit the convolutional nature of the expansion. To address large-scale and streaming data, an online version of VB is also developed. The number of dictionary elements at each layer is inferred from the data, based on a beta-Bernoulli implementation of the Indian buffet process. Example results are presented for several image-processing applications, with comparisons to related models in the literature.

 

dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320