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| Presentation | 2017-07-13 13:25 On the Efficiency of Limited-Memory quasi-Newton Training using Second-Order Approximation Gradient Model with Inertial Term Shahrzad Mahboubi, Hiroshi Ninomiya (SIT)  NLP2017-32 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In recent years, along with large-scale data, it is expected that the scale of neural network will be large too. Therefore, the amount of memory becomes enormous as the scale of the parameter of learning becomes huge. To deal with this problem, it is noteworthy that quasi-Newton algorithm incorporating Limited-memory method is effective for large-scale optimization problems. In this paper, we focus on Second-order approximation gradient model with inertial term incorporating Limited-memory scheme. We proposed the quasi-Newton method using Second-order approximation gradient model with inertial term as Nestelov's accelerated quasi-Newton method, improving the convergence speed of training. The effectiveness of Limited-memory scheme for Nestelov's accelerated quasi-Newton method is studied in this research. We apply the proposed method to training of the neural network and show effectiveness using computer simulations. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Limited-memory quasi-Newton method / Second-order approximation gradient model with inertial term / Nestelov’s accelerated quasi-Newton method / neural network / training algorithm / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 117, no. 121, NLP2017-32, pp. 23-28, July 2017. | |
| Paper # | NLP2017-32 | |
| Date of Issue | 2017-07-06 (NLP) | |
| 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 | NLP2017-32 |
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| Conference Information | |
| Committee | NLP |
| Conference Date | 2017-07-13 - 2017-07-14 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Miyako Island Marine Terminal |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | etc. |
| Paper Information | |
| Registration To | NLP |
| Conference Code | 2017-07-NLP |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | On the Efficiency of Limited-Memory quasi-Newton Training using Second-Order Approximation Gradient Model with Inertial Term |
| Sub Title (in English) | |
| Keyword(1) | Limited-memory quasi-Newton method |
| Keyword(2) | Second-order approximation gradient model with inertial term |
| Keyword(3) | Nestelov’s accelerated quasi-Newton method |
| Keyword(4) | neural network |
| Keyword(5) | training algorithm |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Shahrzad Mahboubi |
| 1st Author's Affiliation | Shonan Institute of Technology University (SIT) |
| 2nd Author's Name | Hiroshi Ninomiya |
| 2nd Author's Affiliation | Shonan Institute of Technology University (SIT) |
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| Speaker | Author-1 |
| Date Time | 2017-07-13 13:25:00 |
| Presentation Time | 25 minutes |
| Registration for | NLP |
| Paper # | NLP2017-32 |
| Volume (vol) | vol.117 |
| Number (no) | no.121 |
| Page | pp.23-28 |
| #Pages | 6 |
| Date of Issue | 2017-07-06 (NLP) |
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