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Inspired by the success of Convolutional Neural Networks (CNNs) for supervised prediction in images, we design the Deconvolutional Generative Model (DGM), a new probabilistic generative model whose inference calculations correspond to those in a given CNN architecture.
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Y. Gal and Z. Ghahramani · 2016
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K. He, X. Zhang, S. Ren, and J. Sun · 2016
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K. Kawaguchi · 2016
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S. Mallat · 2016
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Semi-supervised learning with the deep rendering mixture model
T. Nguyen, W. Liu, E. Perez, R. G. Baraniuk, and A. B. Patel · 2016
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V. Papyan, Y. Romano, and M. Elad · 2017
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Working locally thinking globally: Theoretical guarantees for convolutional sparse coding
V. Papyan, J. Sulam, and M. Elad · 2017
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A. Tarvainen and H. Valpola · 2017
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Information dropout: Learning optimal representations through noisy computation
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On the optimization of deep networks: Implicit acceleration by overparameterization
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