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| Presentation | 2022-03-02 11:00 Learning of a stacked autoencoder with regularizers added to the cost function, evaluation of their effectiveness, and clarification of its information compression mechanism Masumi Ishikawa (Kyutech)  NC2021-49 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Deep learning has a serious drawback in that the resulting models tend to be a black box, hence hard to understand. A sparse modeling approach is expected to ameliorate the drawback. Various regularization terms are proposed so far. The paper proposes to use the concept of Pareto optimality composed of data fitting and the sparseness of models for judging the effectiveness of regularization terms using DB such as US census data. We have demonstrated that compared to the most popular L1-norm to connection weights, the selective L1-norm to connection weights is more effective, the selective L1 norm or L2 norm to hidden outputs and KL divergence or off-diagonal squared covariance of hidden outputs are yet more effective. This enables the clarification of information compression mechanism of the resulting stacked autoencoders. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Deep learning / Black-box model / Stacked autoencoder / Explainable / Sparse modeling / Regularizers / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 390, NC2021-49, pp. 17-22, March 2022. | |
| Paper # | NC2021-49 | |
| Date of Issue | 2022-02-23 (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 | NC2021-49 |
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| Conference Information | |
| Committee | MBE NC |
| Conference Date | 2022-03-02 - 2022-03-04 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2022-03-MBE-NC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Learning of a stacked autoencoder with regularizers added to the cost function, evaluation of their effectiveness, and clarification of its information compression mechanism |
| Sub Title (in English) | |
| Keyword(1) | Deep learning |
| Keyword(2) | Black-box model |
| Keyword(3) | Stacked autoencoder |
| Keyword(4) | Explainable |
| Keyword(5) | Sparse modeling |
| Keyword(6) | Regularizers |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Masumi Ishikawa |
| 1st Author's Affiliation | Kyushu Institute of Technology (Kyutech) |
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| Speaker | Author-1 |
| Date Time | 2022-03-02 11:00:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2021-49 |
| Volume (vol) | vol.121 |
| Number (no) | no.390 |
| Page | pp.17-22 |
| #Pages | 6 |
| Date of Issue | 2022-02-23 (NC) |
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