The Limitation and Practical Acceleration of Stochastic Gradient Algorithms in Inverse Problems

In this work we investigate the practicability of stochastic gradient descent and recently introduced variants with variance-reduction techniques in imaging inverse problems, such as space-varying image deblurring. Such algorithms have been shown in machine learning literature to have optimal comple...

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Vydané v:	Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 7680 - 7684
Hlavní autori:	Tang, Junqi, Egiazarian, Karen, Davies, Mike
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.05.2019
Predmet:	Acceleration Convergence Gradient methods Image Processing Inverse problems Machine learning Stochastic Optimization Task analysis
ISSN:	2379-190X
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