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This paper addresses the understanding and characterization of residual networks (ResNet), which are among the state-of-the-art deep learning architectures for a variety of supervised learning problems.
Methods of Numerical Integration
P. J. Davis and P. Rabinowitz · 1984
Earlier work this paper cites.
Diffeomorphisms Groups and Pattern Matching in Image Analysis
A. Trouvé · 1998
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Learning with Kernels
B. Scholkopf and A. J. Smola · 2002
Earlier work this paper cites.
Geodesic estimation for large deformation anatomical shape averaging and interpolation
B. Avants and J. C. Gee · 2004
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Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms
M. F. Beg, M. I. Miller, A. Trouvé, and L. Younes · 2005
Earlier work this paper cites.
Processing Data in Lie Groups: An Algebraic Approach. Application to Non-Linear Registration and Diffusion Tensor MRI
V. Arsigny · 2006
Earlier work this paper cites.
A Log-Euclidean Framework for Statistics on Diffeomorphisms
V. Arsigny, O. Commowick, X. Pennec, and N. Ayache · 2006
Earlier work this paper cites.
A fast diffeomorphic image registration algorithm
J. Ashburner · 2007
Earlier work this paper cites.
Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration
A. Klein, J. Andersson, B. A. Ardekani, J. Ashburner, B. Avants, M.-C. Chiang, G. E. Christensen, D. L. Collins, J. Gee, P. Hellier, J. H. Song, M. Jenkinson, C. Lepage, D. Rueckert, P. Thompson, T. Vercauteren, R. P. Woods, J. J. Mann, and R. V. Parsey · 2009
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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Shapes and Diffeomorphisms
L. Younes · 2010
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Deformable medical image registration: a survey
A. Sotiras, C. Davatzikos, and N. Paragios · 2013
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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Adam: A Method for Stochastic Optimization
D. P. Kingma and J. Ba · 2014
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
J. Kim, J. K. Lee, and K. M. Lee · 2016
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Deeply-Recursive Convolutional Network for Image Super-Resolution
J. Kim, J. K. Lee, and K. M. Lee · 2016
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Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex
Q. Liao and T. A. Poggio · 2016
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Understanding deep convolutional networks
S. Mallat · 2016
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Stable Architectures for Deep Neural Networks
E. Haber and L. Ruthotto · 2017
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Principles of Riemannian Geometry in Neural Networks
M. Hauser and A. Ray · 2017
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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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Image matching as a diffusion process: an analogy with Maxwell’s demons
J. Thirion
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A proposal on machine learning via dynamical systems
E. Weinan and 2017 · 2017
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Deep Neural Networks motivated by Partial Differential Equations
L. Ruthotto and E. Haber · 2018
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