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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 27  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
SeMI, RCS, NS, SR, RCC
(Joint)
2019-07-10
16:45
Osaka I-Site Nanba(Osaka) [Invited Talk] Current Status of Reinforcement Learning -- Algorithms and Applications --
Shin-ichi Maeda (PFN) RCC2019-18 NS2019-51 RCS2019-108 SR2019-27 SeMI2019-27
Reinforcement Learning is a framework to optimize an action sequence in terms of the return maximization. In this talk, ... [more] RCC2019-18 NS2019-51 RCS2019-108 SR2019-27 SeMI2019-27
p.39(RCC), p.49(NS), p.43(RCS), p.49(SR), p.53(SeMI)
IBISML 2016-03-17
16:10
Tokyo Institute of Statistical Mathematics Bayesian Monte-Carlo tree search method and its application to linear constrained nonlinear control problems
Ryo Otsuki, Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) IBISML2015-98
For the nonlinear dynamics where the state transition is nonlinear with respect to the input, in general, we cannot obta... [more] IBISML2015-98
pp.31-38
IBISML 2015-11-27
14:00
Ibaraki Epochal Tsukuba [Poster Presentation] Regularization by local distributional smoothing
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii (Kyoto Univ.) IBISML2015-87
Smoothness regularization is a popular method to decrease generalization error. We propose a novel regularization techni... [more] IBISML2015-87
pp.257-264
IBISML 2015-11-27
14:00
Ibaraki Epochal Tsukuba [Poster Presentation] Bayesian Masking for Sparse Feature Selection
Yohei Kondo (Kyoto Univ.), Kohei Hayashi (NII), Shin-ichi Maeda (Kyoto Univ.) IBISML2015-88
In linear regression, we can reduce the weights of irrelevant features by L2 or L1 regularization. However, such a regul... [more] IBISML2015-88
pp.265-272
NC, MBE
(Joint)
2014-03-18
14:00
Tokyo Tamagawa University Fusion of Multiple Cues from Color and Depth Domains using Occlusion Aware Bayesian Tracker
Kourosh Meshgi, Shin-ichi Maeda, Shigeyuki Oba, Shin Ishii (Kyoto Univ.) NC2013-110
 [more] NC2013-110
pp.127-132
NC, MBE
(Joint)
2014-03-18
14:20
Tokyo Tamagawa University 3D Superresolution of Microscopic Images based on Variational Bayesian Inference via Chebyshev polynomials approximation
Yasuhiro Imoto, Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) NC2013-111
Optical microscopes are used to elucidate changes in cellular functions mediated by morphological changes of cells in vi... [more] NC2013-111
pp.133-138
NC, MBE
(Joint)
2014-03-18
13:00
Tokyo Tamagawa University Layered Monte-Carlo planning method for incomplete information games and its application to Puyo-Puyo
Ryo Otsuki, Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) NC2013-136
Recently, the effectiveness of the game-tree search algorithm based on UCT and its applicability to the game Go have bee... [more] NC2013-136
pp.275-280
NC, MBE
(Joint)
2014-03-18
15:00
Tokyo Tamagawa University Human Activity Rcognition with Skeleton-data and Structured Hierarchical Hidden Markov Model
Tomoji Sawada, Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) NC2013-141
Recognition of human activities nowadays is used not only for monitoring system, but also interface,
motion analysis of... [more]
NC2013-141
pp.305-310
PRMU, IBISML, IPSJ-CVIM [detail] 2013-09-02
17:30
Tottori   Enhancing Probabilistic Appearance-Based Object Tracking with Depth Information -- Object Tracking under Occlusion --
Kourosh Meshgi, Yu-zhe Li, Shigeyuki Oba, Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) PRMU2013-42 IBISML2013-22
Object tracking has attracted recent attention because of high demands for its everyday-life applications. Handling occl... [more] PRMU2013-42 IBISML2013-22
pp.85-91
NC, NLP 2013-01-24
10:30
Hokkaido Hokkaido University Centennial Memory Hall Control of the falling cat motion by using path-integral reinforcement learning
Daichi Nakano, Shin-ichi Maeda, Shin Ishii (Kyoto Univ) NLP2012-107 NC2012-97
The falling-cat motion is a motion for controlling the cat's posture under no existence of external force. To obtain a c... [more] NLP2012-107 NC2012-97
pp.19-24
NC, NLP 2013-01-24
10:50
Hokkaido Hokkaido University Centennial Memory Hall Significance of non-stationary of dynamics for learning cooperative behavior
Akihiro Tawa, Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) NLP2012-108 NC2012-98
To understand how cooperative behaviors emerge is important in the field of multi-agent system research. Although this e... [more] NLP2012-108 NC2012-98
pp.25-30
NC, NLP 2013-01-24
11:10
Hokkaido Hokkaido University Centennial Memory Hall depth estimation from microscopic images using Bayesian inference
Yasuhiro Imoto, Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) NLP2012-109 NC2012-99
In cellular biology, it is important to know 3D cellular shape to understand the cellular function. However, existing mi... [more] NLP2012-109 NC2012-99
pp.31-36
MI 2010-11-15
13:25
Kyoto Shimadzu Corp. Bayesian modeling of medical X-ray computed tomography
Shin-ichi Maeda (Kyoto Univ.), Atsunori Kanemura (ATR), Shin Ishii (Kyoto Univ.) MI2010-73
The tradeoff between the resolution of CT images and the amount of exposure to radiation leads us to desire a CT algorit... [more] MI2010-73
pp.39-44
IBISML 2010-06-15
15:10
Tokyo Takeda Hall, Univ. Tokyo Generalization of TD-learning from a Semiparametric Statistical Viewpoint
Tsuyoshi Ueno, Shin-ichi Maeda (Kyoto Univ.), Motoaki Kawanabe (Fraunhofer First), Shin Ishii (Kyoto Univ.) IBISML2010-20
(Advance abstract in Japanese is available) [more] IBISML2010-20
pp.131-138
NC, MBE
(Joint)
2010-03-10
11:05
Tokyo Tamagawa University Bayesian X-ray Computed Tomography Using a Mixture Prior
Wataru Fukuda, Shin-ichi Maeda, Atsunori Kanemura, Shin Ishii (Kyoto Univ.) NC2009-133
 [more] NC2009-133
pp.267-272
NC, MBE
(Joint)
2010-03-11
11:05
Tokyo Tamagawa University Learning of Go board state evaluation by online adaptive natural gradient method
Hiroki Tomizawa, Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) NC2009-164
We propose a supervised learning of Go board state evaluation function with many game records of experts'. By training a... [more] NC2009-164
pp.449-454
SIP, CAS, CS 2010-03-02
13:45
Okinawa Hotel Breeze Bay Marina, Miyakojima [Poster Presentation] Geometrical analysis of linear discriminant analysis algorithms and instrument feature extraction
Mizuki Ihara, Kazushi Ikeda (NAIST), Shin-ichi Maeda (Kyoto Univ.) CAS2009-123 SIP2009-168 CS2009-118
Extracting only the essential sound attributes from sounds is one of the fundamental issues of music information retriev... [more] CAS2009-123 SIP2009-168 CS2009-118
pp.243-244
NC, MBE
(Joint)
2009-03-11
16:35
Tokyo Tamagawa Univ. A Learning Algorithm of Helmholtz Machine with Mean Field Approximation
Yuki Aoki (Nara Inst. of Sci and Tech.), Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) NC2008-113
It is often required to extract a compact feature of an original high-dimensional datum. Such a compactfeature is useful... [more] NC2008-113
pp.57-62
NC, MBE
(Joint)
2009-03-12
15:15
Tokyo Tamagawa Univ. Semiparametric Statistical Approach to Value Function Estimation
Tsuyoshi Ueno (Kyoto Univ.), Motoaki Kawanabe (Fraunhofer First), Takeshi Mori, Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) NC2008-146
Recently least-squares
temporal difference (LSTD) learning
has been developed
for the model-free value function es... [more]
NC2008-146
pp.255-260
NC, MBE
(Joint)
2009-03-13
13:00
Tokyo Tamagawa Univ. Superresolution from Occluded Scenes
Wataru Fukuda, Atsunori Kanemura, Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) NC2008-155
 [more] NC2008-155
pp.307-312
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