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Model-based methods and deep neural networks have both been tremendously successful paradigms in machine learning.
Vision: A computational investigation into the human representation and processing of visual information
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P. D. O’Grady and B. A. Pearlmutter, · 2007
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C. Févotte, N. Bertin, and J.-L. Durrieu, · 2009
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“Norm-product belief propagation: Primal-dual message-passing for approximate inference,”
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T. B. Yakar, R. Litman, P. Sprechmann, A. Bronstein, and G. Sapiro, · 2013
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“Learning graphical model parameters with approximate marginal inference.,”
J. Domke, · 2013
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“Multi-prediction deep boltzmann machines,”
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I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio, · 2013
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C. Févotte, J. Le Roux, and J. R. Hershey, · 2013
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“Discriminative NMF and its application to single-channel source separation,”
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J. Domke, · 2011
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“Learning message-passing inference machines for structured prediction,”
S. Ross, D. Munoz, M. Hebert, and J. A. Bagnell, · 2011
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Q. Liu and A. T. Ihler, · 2012
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J. Mairal, F. Bach, and J. Ponce, · 2012
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A. Ozerov, E. Vincent, and F. Bimbot, · 2012
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G. J. Mysore and M. Sahani, · 2012
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P. Sprechmann, R. Litman, T. B. Yakar, A. M. Bronstein, and G. Sapiro, · 2013
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F. Weninger, J. Le Roux, J. R. Hershey, and S. Watanabe, · 2014
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“Supervised non-euclidean sparse NMF via bilevel optimization with applications to speech enhancement,”
P. Sprechmann, A. M. Bronstein, and G. Sapiro, · 2014
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“Mean field networks,”
Y. Li and R. Zemel, · 2014
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X. Zhang, J. Trmal, D. Povey, and S. Khudanpur, · 2014
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P.-S. Huang, M. Kim, M. Hasegawa-Johnson, and P. Smaragdis, · 2014
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“Non-negative source-filter dynamical system for speech enhancement,”
U. Simsekli, J. Le Roux, and J. R. Hershey, · 2014
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