|
|
All Technical Committee Conferences (Searched in: All Years)
|
|
Search Results: Conference Papers |
Conference Papers (Available on Advance Programs) (Sort by: Date Descending) |
|
Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
ASN, NS, RCS, SR, RCC (Joint) |
2018-07-12 15:05 |
Hokkaido |
Hakodate Arena |
[Tutorial Lecture]
Reinforcement Learning: Application and Issues Takaki Makino (Google) RCC2018-51 NS2018-68 RCS2018-113 SR2018-48 ASN2018-45 |
Reinforcement learning is a machine learning framework that learns best sequence of actions through trial-and-error in d... [more] |
RCC2018-51 NS2018-68 RCS2018-113 SR2018-48 ASN2018-45 p.119(RCC), p.151(NS), p.161(RCS), p.129(SR), p.135(ASN) |
CAS, NLP |
2013-09-26 15:55 |
Gifu |
Satellite Campus, Gifu University |
[Invited Talk]
Theoretical Analysis on Quantization Error of β-Encoder Takaki Makino (Univ. of Tokyo), Yukiko Iwata (Meteorol. College), Yutaka Jitsumatsu (Kyushu Univ.), Masao Hotta, Hao San (Tokyo City Univ.), Kazuyuki Aihara (Univ. of Tokyo) CAS2013-43 NLP2013-55 |
Theoretical evaluation of last{the quantization} error of the $beta$-encoder, that is a non-binary analog-to-digital con... [more] |
CAS2013-43 NLP2013-55 pp.41-44 |
IBISML |
2012-11-07 15:30 |
Tokyo |
Bunkyo School Building, Tokyo Campus, Tsukuba Univ. |
Regularization of Restricted Boltzmann Machine Learning through entropy minimization Taichi Kiwaki, Takaki Makino, Kazuyuki Aihara (Univ. Tokyo) IBISML2012-48 |
We propose a learning scheme for Restricted Boltzmann Machines (RBMs) that suppresses over-fitting, where the entropy of... [more] |
IBISML2012-48 pp.103-106 |
IBISML |
2012-11-08 15:00 |
Tokyo |
Bunkyo School Building, Tokyo Campus, Tsukuba Univ. |
Auto-Colorization of Monochrome Images using Object Recognition and Markov Random Field Hitoshi Matsuo, Takaki Makino (Univ. Tokyo) IBISML2012-83 |
We introduce a new auto-colorization method which appends color information to a monochrome picture based on a training ... [more] |
IBISML2012-83 pp.351-358 |
IBISML |
2012-03-12 15:30 |
Tokyo |
The Institute of Statistical Mathematics |
Apprenticeship Learning for Model Parameters of Partially Observable Environments Takaki Makino (Univ. of Tokyo), Johane Takeuchi (HRI-JP) IBISML2011-94 |
We consider apprentice learning, i.e., to make an agent learn a task by observing an expert demonstrating the task, in a... [more] |
IBISML2011-94 pp.49-54 |
IBISML |
2011-11-09 15:45 |
Nara |
Nara Womens Univ. |
Modified Newton Approach to Policy Search Hirotaka Hachiya (Tokyo Inst. of Tech.), Tetsuro Morimura (IBM Japan), Takaki Makino (Univ. of Tokyo), Masashi Sugiyama (Tokyo Inst. of Tech.) IBISML2011-54 |
The natural policy gradient method was shown to be a useful approach to policy search in reinforcement learning. However... [more] |
IBISML2011-54 pp.79-85 |
IBISML |
2010-06-15 09:30 |
Tokyo |
Takeda Hall, Univ. Tokyo |
[Invited Talk]
Statistical Machine Learning Based on Nonparametric Bayesian Models Takaki Makino (Univ. of Tokyo.) IBISML2010-14 |
Nonparametric Bayesian models are a new approach for machine learning, involving overfitting avoidance and model selecti... [more] |
IBISML2010-14 pp.87-94 |
|
|
|
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)
|
[Return to Top Page]
[Return to IEICE Web Page]
|