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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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