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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 7 of 7  /   
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
 Results 1 - 7 of 7  /   
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