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| Presentation | 2020-08-28 10:55 Theoretical Analysis on Convergence Acceleration of Deep-Unfolded Gradient Descent Satoshi Takabe, Tadashi Wadayama (NITech)  SIP2020-35 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Deep unfolding is a promising deep learning technique whose network architecture is based on existing iterative algorithms. By unfolding the recursive structure of an iterative algorithm and embedding trainable parameters, deep unfolding can learn the parameters, which results in improving the convergence performance such as convergence speed. The goal of this paper is to give a plausible interpretation of convergence acceleration based on theoretical analyses of deep-unfolded gradient descent (DUGD). As a result, we will introduce Chebyshev steps based on Chebyshev polynomials, which well reproduce learned step sizes in DUGD and improve the convergence rate of GD. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | deep learning / deep unfolding / gradient descent / convergence rate / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 142, SIP2020-35, pp. 25-30, Aug. 2020. | |
| Paper # | SIP2020-35 | |
| Date of Issue | 2020-08-20 (SIP) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | SIP2020-35 |
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| Conference Information | |
| Committee | SIP |
| Conference Date | 2020-08-27 - 2020-08-28 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | SIP |
| Conference Code | 2020-08-SIP |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Theoretical Analysis on Convergence Acceleration of Deep-Unfolded Gradient Descent |
| Sub Title (in English) | |
| Keyword(1) | deep learning |
| Keyword(2) | deep unfolding |
| Keyword(3) | gradient descent |
| Keyword(4) | convergence rate |
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| 1st Author's Name | Satoshi Takabe |
| 1st Author's Affiliation | Nagoya Institute of Technology (NITech) |
| 2nd Author's Name | Tadashi Wadayama |
| 2nd Author's Affiliation | Nagoya Institute of Technology (NITech) |
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| Speaker | Author-1 |
| Date Time | 2020-08-28 10:55:00 |
| Presentation Time | 25 minutes |
| Registration for | SIP |
| Paper # | SIP2020-35 |
| Volume (vol) | vol.120 |
| Number (no) | no.142 |
| Page | pp.25-30 |
| #Pages | 6 |
| Date of Issue | 2020-08-20 (SIP) |
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