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We develop new theoretical results on matrix perturbation to shed light on the impact of architecture on the performance of a deep network.
The variation of the spectrum of a normal matrix
A. J. Hoffman and H. W. Wielandt · 1953
Earlier work this paper cites.
Eigenvalues and condition numbers of random matrices
Alan Edelman · 1988
Earlier work this paper cites.
Level-spacing distributions and the airy kernel
Craig A. Tracy and Harold Widom · 1994
Earlier work this paper cites.
Convolutional networks for images, speech, and time-series
Y. LeCun and Y. Bengio · 1995
Earlier work this paper cites.
Adaptive estimation of a quadratic functional by model selection
B. Laurent and P. Massart · 2000
Earlier work this paper cites.
Distribution functions for largest eigenvalues and their applications
Craig A. Tracy and Harold Widom · 2002
Earlier work this paper cites.
Singular values of random matrices
D. Chafai, O. Guedon, G. Lecue, and A. Pajor · 2009
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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A short note on the operator norm upper bound for sub-Gaussian tailed random matrices
Eric Benhamou, Jamal Atif, and Rida Laraki · 2019
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Mad max: Affine spline insights into deep learning
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K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Neural network approximation of refinable functions
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Evaluation of neural architectures trained with square loss vs. cross-entropy in classification tasks
L. Hui and M. Belkin · 2021
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