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| Presentation | 2017-11-10 13:00 Analysis of Dropout in online learning Kazuyuki Hara (Nihon Univ.)  IBISML2017-61 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Deep learning is the state-of-the-art in fields such as visual object recognition and speech recognition. This learning uses a large number of layers and a huge number of units and connections. Therefore, overfitting is a serious problem with it, and the dropout which is a kind of regularization tool is used. However, in online learning, the effect of dropout is not well known. This paper presents our investigation on the effect of dropout in online learning. We analyzed the effect of dropout on convergence speed near the singular point. Our results indicated that dropout is effective in online learning. Dropout tends to avoid the singular point for convergence speed near that point. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Deep Learning / Dropout / Slow dynamics / Multilayer perceptron / Teacher-Student formulation / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 117, no. 293, IBISML2017-61, pp. 201-206, Nov. 2017. | |
| Paper # | IBISML2017-61 | |
| Date of Issue | 2017-11-02 (IBISML) | |
| ISSN | Print edition: ISSN 0913-5685 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 | IBISML2017-61 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2017-11-08 - 2017-11-10 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Univ. of Tokyo |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Information-Based Induction Science Workshop (IBIS2017) |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2017-11-IBISML |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Analysis of Dropout in online learning |
| Sub Title (in English) | |
| Keyword(1) | Deep Learning |
| Keyword(2) | Dropout |
| Keyword(3) | Slow dynamics |
| Keyword(4) | Multilayer perceptron |
| Keyword(5) | Teacher-Student formulation |
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| 1st Author's Name | Kazuyuki Hara |
| 1st Author's Affiliation | Nihon University (Nihon Univ.) |
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| Speaker | 1 |
| Date Time | 2017-11-10 13:00:00 |
| Presentation Time | 150 |
| Registration for | IBISML |
| Paper # | IEICE-IBISML2017-61 |
| Volume (vol) | IEICE-117 |
| Number (no) | no.293 |
| Page | pp.201-206 |
| #Pages | IEICE-6 |
| Date of Issue | IEICE-IBISML-2017-11-02 |
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