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All Technical Committee Conferences (Searched in: All Years)
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Search Results: Conference Papers |
Conference Papers (Available on Advance Programs) (Sort by: Date Descending) |
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Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
IBISML |
2018-11-05 15:10 |
Hokkaido |
Hokkaido Citizens Activites Center (Kaderu 2.7) |
[Poster Presentation]
Learning Restricted Boltzmann Machine via the Adaptive Thouless-Anderson-Palmer Mean-Field Approximation Chako Takahashi (Tohoku Univ.), Muneki Yasuda (Yamagata Univ.), Kazuyuki Tanaka (Tohoku Univ.) IBISML2018-58 |
(Advance abstract in Japanese is available) [more] |
IBISML2018-58 pp.105-110 |
SP, IPSJ-SLP (Joint) |
2013-07-26 14:20 |
Miyagi |
Soho (togatta spa) |
[Invited Talk]
An Introduction to Deep Learning
-- Restricted Boltzmann Machine and Pre-Training -- Muneki Yasuda (Yamagata Univ.) SP2013-60 |
Deep learnings are learnings on multi-layered learning models.
Deep learnings have achieved astounding success in the f... [more] |
SP2013-60 pp.45-49 |
MBE, NC (Joint) |
2012-11-17 13:30 |
Miyagi |
Tohoku University |
Composite likelihood estimation for bipartite Boltzmann machines Takashi Asari, Muneki Yasuda, Yuji Waizumi, Kazuyuki Tanaka (Tohoku Univ.) NC2012-68 |
The recent development of information technology has enabled us to obtain and to storage huge information data.
Because... [more] |
NC2012-68 pp.39-44 |
MBE, NC (Joint) |
2012-11-17 14:55 |
Miyagi |
Tohoku University |
Design of probabilistic image inpainting filters using Gaussian graphial models Tomotaka Kitagawa, Muneki Yasuda, Kazuyuki Tanaka (Tohoku Univ.) NC2012-71 |
A probabilistic image inpainting lter is an image processing lter that reconstructs lost or deteriorated pixels of ima... [more] |
NC2012-71 pp.57-62 |
MBE, NC (Joint) |
2012-03-16 13:20 |
Tokyo |
Tamagawa University |
Item Response Theory with Correlations among Items Muneki Yasuda, Kazuyuki Tanaka (Tohoku Univ.) NC2011-187 |
Item response theory (IRT) is a recent test theory which has been developed in mainly psychology and social science.
A ... [more] |
NC2011-187 pp.387-391 |
IBISML |
2011-11-09 15:45 |
Nara |
Nara Womens Univ. |
Gaussian FoE model with correlations among color components Ryuta Murayama, Muneki Yasuda, Yuji Waizumi, Kazuyuki Tanaka (Tohoku Univ.) IBISML2011-58 |
Color images consist of some color components such as RGB, YCbCr. They have been often treated independently in usual im... [more] |
IBISML2011-58 pp.105-111 |
IBISML |
2010-11-04 15:00 |
Tokyo |
IIS, Univ. of Tokyo |
[Poster Presentation]
Advanced Susceptibility Propagation Muneki Yasuda, Kazuyuki Tanaka (Tohoku Univ.) IBISML2010-67 |
Inferences in Markov random fields are ones of the most important problems in information science.
Susceptibility prop... [more] |
IBISML2010-67 pp.59-63 |
IBISML |
2010-11-05 15:30 |
Tokyo |
IIS, Univ. of Tokyo |
[Poster Presentation]
Statistical Mechanical Informatics for Portfolio Optimization Problems Takashi Shinzato (Akita Pref. Univ.), Muneki Yasuda (Tohoku Univ.) IBISML2010-94 |
(Advance abstract in Japanese is available) [more] |
IBISML2010-94 pp.257-263 |
NC, MBE (Joint) |
2009-03-12 14:15 |
Tokyo |
Tamagawa Univ. |
Learning algorithm in Boltzmann machines using the belief propagation algorithm Junya Tannai, Muneki Yasuda, Kazuyuki Tanaka (Tohoku Univ.) NC2008-144 |
Boltzmann machines are stochastic neural networks defined on undirected graphs and are expected to be learning machines ... [more] |
NC2008-144 pp.243-248 |
NC, MBE (Joint) |
2008-03-12 10:00 |
Tokyo |
Tamagawa Univ |
Gaussian Graphical Model on Scale-Free Network Takafumi Usui, Muneki Yasuda, Kazuyuki Tanaka (Tohoku Univ.) NC2007-112 |
We consider probabilistic inferences formulated by using Gaussian graphical models on scale free networks.
We can deriv... [more] |
NC2007-112 pp.1-5 |
NC, MBE (Joint) |
2008-03-12 13:30 |
Tokyo |
Tamagawa Univ |
Image Processing by using the EM algorithm and the belief propagation Kei Inoue, Muneki Yasuda, Kazuyuki Tanaka (Tohoku Univ.) NC2007-118 |
Markov random fields in image processing includehyperparameters to estimate from given data. We introduce a method to es... [more] |
NC2007-118 pp.37-42 |
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