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Committee Date Time Place Paper Title / Authors Abstract Paper #
SIS, IPSJ-AVM, ITE-3DMT [detail] 2019-06-13
11:20		 Nagasaki Fukue Culture Center A random number generation method for hardware implemented neural networks
Sansei Hori, Hakaru Tamukoh (Kyushu Inst. of Tech.) SIS2019-1		 This study proposes a hardware oriented random number generation method to implement a stochastically neural networks su... [more] SIS2019-1
pp.1-4
NC, MBE 2015-03-16
15:10		 Tokyo Tamagawa University A Proposal of Novel Data Detection Method and Its Application to Incremental Learning for RBMs
Masahiko Osawa, Masafumi Hagiwara (Keio Univ.) MBE2014-167 NC2014-118		 Incremental learnings without destruction of the existing memory are often difficult for deep learning, since most of th... [more] MBE2014-167 NC2014-118
pp.283-288
IBISML 2015-03-05
16:15		 Kyoto Kyoto University Adaptation of Machine Learning Method for Music Structure Analysis
Yoshiyuki Kushibe, Toshiaki Takita (Univ. of Tsukuba), Masatoshi Hamanaka (Kyoto Univ.), Sakurako Yazawa, Junichi Hoshino (Univ. of Tsukuba) IBISML2014-89		 This paper describes the music structure analysis method using machine learning. Music structure analysis is to automati... [more] IBISML2014-89
pp.31-38
IBISML 2014-11-18
15:00		 Aichi Nagoya Univ. [Poster Presentation] Denoising High-dimensional Sequences with the Bidirectional Recurrent Restricted Boltzmann Machine
Shoken Kaneko (Yamaha Co.) IBISML2014-62		 We propose a probabilistic neural network for modeling high-dimensional sequences with complex non-linearities.
Our mod... [more]		 IBISML2014-62
pp.207-212
MBE, NC
(Joint)		 2014-10-18
14:50		 Osaka Osaka Electro-Communication University Analysis of Learning Characteristics of RBM and Automatic Method for Deciding the Number of Hidden Neurons
Masahiko Osawa, Masafumi Hagiwara (Keio Univ.) NC2014-22		 In this paper，we analyze the learning characteristics of Restricted Boltzmann Machine (RBM) by computer simulation． Then... [more] NC2014-22
pp.7-12
NC, IPSJ-BIO [detail] 2011-06-24
16:30		 Okinawa 50th Anniversary Memorial Hall, University of the Ryukyus Solving POMDPs using Restricted Boltzmann Machines with Echo State Networks
Makoto Otsuka, Junichiro Yoshimoto, Stefan Elfwing, Kenji Doya (OIST) NC2011-19		 A partially observable Markov decision process (POMDP) can be solved in a model-based way using explicit knowledge of th... [more] NC2011-19
pp.143-148
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