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In this work we establish the relation between optimal control and training deep Convolution Neural Networks (CNNs).
Learning Long-Term Dependencies with Gradient Descent Is Difficult
Y. BENGIO, P. SIMARD, and P. FRASCONI · 1994
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Convolutional networks for images, speech, and time series
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The cascadic multigrid method for elliptic problems
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Multigrid
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Multilevel methods for image registration
E. Haber and J. Modersitzki · 2004
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Numerical Methods for Image Registration
J. Modersitzki · 2004
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Real-Time PDE-Constrained Optimization
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FAIR: Flexible Algorithms for Image Registration
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Large-scale video classification with convolutional neural networks
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei · 2014
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Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. A. Riedmiller · 2014
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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A general framework for constrained bayesian optimization using information-based search
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J. M. Hernández-Lobato, M. A. Gelbart, R. P. Adams, M. W. Hoffman, and Z. Ghahramani · 2016
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