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| Presentation | 2019-03-04 17:00 Towards understandable deep learning in stacked autoencoders Masumi Ishikawa (Kyutech)  NC2018-62 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recent progress of deep learning(DP) is remarkable and its recognition ability is said to surpass that of humans. The acquired features, however, are not clear to humans. In many cases, basis of judgement by DP is not known. In 1990s the author proposed l1-norm to make neural networks understandable. In DP, nonlinear in nature, l1-norm alone is not enough to understand the resulting model due to distributed representations. The present paper proposes to extend the previous proposal by jointly using selective l1-norm, hidden neurons activation regularization and so forth, and applies it to real data to verify that sparsity obtained and meaning of hidden neurons attached are effective. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Deep learning / Stacked autoencoders / Understandable AI / l1- norm / Neural networks / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 470, NC2018-62, pp. 99-104, March 2019. | |
| Paper # | NC2018-62 | |
| Date of Issue | 2019-02-25 (NC) | |
| 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 | NC2018-62 |
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| Conference Information | |
| Committee | NC MBE |
| Conference Date | 2019-03-04 - 2019-03-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | University of Electro Communications |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2019-03-NC-MBE |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Towards understandable deep learning in stacked autoencoders |
| Sub Title (in English) | |
| Keyword(1) | Deep learning |
| Keyword(2) | Stacked autoencoders |
| Keyword(3) | Understandable AI |
| Keyword(4) | l1- norm |
| Keyword(5) | Neural networks |
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| 1st Author's Name | Masumi Ishikawa |
| 1st Author's Affiliation | Kyushu Institute of Technology (Kyutech) |
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| Speaker | Author-1 |
| Date Time | 2019-03-04 17:00:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2018-62 |
| Volume (vol) | vol.118 |
| Number (no) | no.470 |
| Page | pp.99-104 |
| #Pages | 6 |
| Date of Issue | 2019-02-25 (NC) |
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