Paper Abstract and Keywords |
Presentation |
2022-01-23 12:10
Deep learning of mixture of continuous and categorical data with regularizers added to the cost function and evaluation of the effectiveness of sparse modeling Masumi Ishikawa (Kyutech) NC2021-45 |
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 US census data. Compared to the most popular L1-norm to connection weights, the selective L1-norm to connection weights is better, off-diagonal covariance or KL divergence of hidden outputs are yet better, and the selective L1 norm or selective L2 norm to hidden outputs are the best. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Deep learning / Black-box model / Explainable / Sparse modeling / Regularizers / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 338, NC2021-45, pp. 65-70, Jan. 2022. |
Paper # |
NC2021-45 |
Date of Issue |
2022-01-14 (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) |
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NC2021-45 |
Conference Information |
Committee |
NLP MICT MBE NC |
Conference Date |
2022-01-21 - 2022-01-23 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
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Paper Information |
Registration To |
NC |
Conference Code |
2022-01-NLP-MICT-MBE-NC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Deep learning of mixture of continuous and categorical data with regularizers added to the cost function and evaluation of the effectiveness of sparse modeling |
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Deep learning |
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Black-box model |
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Explainable |
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Sparse modeling |
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Regularizers |
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1st Author's Name |
Masumi Ishikawa |
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Kyushu Institute of Technology (Kyutech) |
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Speaker |
Author-1 |
Date Time |
2022-01-23 12:10:00 |
Presentation Time |
25 minutes |
Registration for |
NC |
Paper # |
NC2021-45 |
Volume (vol) |
vol.121 |
Number (no) |
no.338 |
Page |
pp.65-70 |
#Pages |
6 |
Date of Issue |
2022-01-14 (NC) |
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