| IEICE Technical Committee Submission System Conference Schedule |
Online Proceedings [Sign in] Tech. Rep. Archives |
All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| VLD, DC, RECONF, ICD, IPSJ-SLDM [detail] | 2023-11-17 15:40 |
Kumamoto | Civic Auditorium Sears Home Yume Hall (Primary: On-site, Secondary: Online) |
High-Level Synthesis Implementation of a Reservoir Computing based on Chaotic Boltzmann Machine
-- Improving scalability and efficiency of sparse matrix multiplication through a dedicated data compression in external memory -- Shigeki Matsumoto, Yuki Ichikawa, Nobuki Kajihara (IVIS), Hakaru Tamukoh (kyutech)  VLD2023-75 ICD2023-83 DC2023-82 RECONF2023-78 |
This paper reports on an FPGA implementation of Chaotic Boltzmann Machine Reservoir Computing (CBM-RC). The reservoir wi... [more] | VLD2023-75 ICD2023-83 DC2023-82 RECONF2023-78 pp.231-236 |
| NC, MBE (Joint) |
2023-03-14 16:15 |
Tokyo | The Univ. of Electro-Communications (Primary: On-site, Secondary: Online) |
Generation and inpainting of Kuzushiji image data using Boltzmann Machines Hiroki Ikoma (NAIST), Mauricio Bermudez, Minho Lee (KNU), Kazushi Ikeda (NAIST) NC2022-108 |
It is almost impossible for the average person to read Kuzushiji today. For this reason, there is a need to develop tool... [more] | NC2022-108 pp.94-98 |
| SP, IPSJ-MUS, IPSJ-SLP [detail] | 2022-06-17 15:00 |
Online | Online | Blind Source Separation based on Independent Low-Rank Matrix Analysis using Restricted Boltzmann Machines Shotaro Furuta, Takuya Kishida, Toru Nakashika (UEC) SP2022-8 |
In this paper, we propose a new blind source separation method that combines independent low-rank source separation (ILR... [more] | SP2022-8 pp.26-29 |
| IBISML | 2022-01-17 11:00 |
Online | Online | Cluster approximation in quantum Boltzmann machine based on information geometry Masaya Hoshikawa, Tomohiro Ogawa (UEC) IBISML2021-21 |
A Boltzmann Machine (BM) is a model of machine learning which consists of mutually connected probabilistic binary units... [more] |
IBISML2021-21 pp.23-28 |
| 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 |
| ICD | 2018-04-19 13:00 |
Tokyo | [Invited Talk]
VLSI implementation of chaotic Boltzmann machine for deep learning hardware Takashi Morie, Masatoshi Yamaguchi, Ichiro Kawashima, Hakaru Tamukoh (Kyushu Inst. of Tech.) ICD2018-4 |
The Boltzmann machine (BM) model has been proposed as an optimization-problem solver as well as a learning machine using... [more] | ICD2018-4 p.13 |
|
| MBE, NC (Joint) |
2018-03-13 10:00 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Yuma Saito, Tsubasa Ito (Tokyo Tech), Keisuke Ota, Masanori Murayama (RIKEN), Toru Aonishi (Tokyo Tech) NC2017-68 |
Recent rapid progress of imaging techniques such as two-photon microscopes causes the extreme increase in amount of acqu... [more] | NC2017-68 pp.3-8 |
| WBS, IT, ISEC | 2018-03-08 09:25 |
Tokyo | Katsusika Campas, Tokyo University of Science | Information Geometrical Study of Cluster-Model Approximation for Boltzmann Machines Kenta Toyoda, Tomohiro Ogawa (Univ. of Electro- Commu.) IT2017-104 ISEC2017-92 WBS2017-85 |
In learning algorithms for Boltzmann machines, it is necessary but very hard to calculate the expectation of units with ... [more] | IT2017-104 ISEC2017-92 WBS2017-85 pp.7-12 |
| IBISML | 2018-03-06 13:35 |
Fukuoka | Nishijin Plaza, Kyushu University | Applicability of Fast Decimation Algorithm
-- Sparse two-parameter Boltzmann machine as a benchmark function -- Daisuke Motoki, Shohei Watabe, Tetsuro Nikuni (Tokyo Univ. of Science) IBISML2017-103 |
A decimation algorithm was developed by Decelle et al. for an inverse problem optimization method, which sequentially re... [more] | IBISML2017-103 pp.91-95 |
| NLP | 2016-03-24 13:25 |
Kyoto | Kyoto Sangyo Univ. | Implementation of Boltzmann Machine by Asynchronous Network of Cellular Automaton-based Neurons Takashi Matsubara, Kuniaki Uehara (Kove Univ.) NLP2015-143 |
Artificial neural networks with stochastic state transitions, such as Deep Boltzmann Machine, have excelled other machin... [more] | NLP2015-143 pp.7-10 |
| MBE, NC (Joint) |
2016-03-22 11:25 |
Tokyo | Tamagawa University | Construction of Semantic Network Using Restricted Boltzmann Machine Yuichiro Tsutsui, Masafumi Hagiwara (Keio Univ.) NC2015-71 |
In this paper, we propose a new kind of neural network type semantic network utilizing distributed representation. The p... [more] | NC2015-71 pp.13-18 |
| NLC, IPSJ-NL, SP, IPSJ-SLP (Joint) [detail] |
2015-12-02 10:25 |
Aichi | Nagoya Inst of Tech. | Simultaneous Modelling of Acoustic, Phonetic, Speaker Features Using Improved Three-Way Restricted Boltzmann Machine Toru Nakashika (UEC), Tetsuya Takiguchi (Kobe Univ.) SP2015-71 |
In this paper, we argue the way of modelling speech signals using improved three-way restricted Boltzmann machine (3WRBM... [more] | SP2015-71 pp.7-12 |
| 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 13:00 |
Kyoto | Kyoto University | [Invited Talk]
Detection of cheating in Boltzmann machine learning
-- A parameter-free algorithm to sparse solution -- Masayuki Ohzeki (KU) IBISML2014-85 |
We generalize a mathematical model in the item response theory into that in the Boltzmann machine learning to detect “ch... [more] | IBISML2014-85 pp.1-8 |
| 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 | 2014-11-17 17:00 |
Aichi | Nagoya Univ. | [Poster Presentation]
Feature Extraction for Image Classification using Restricted Boltzmann Machines Reiki Suda, Koujin Takeda (Ibaraki Univ.) IBISML2014-36 |
Learning restricted Boltzmann machines (RBMs) for high-dimensional data using maximum likelihood estimation had been fac... [more] | IBISML2014-36 pp.9-15 |
| IBISML | 2014-11-17 17:00 |
Aichi | Nagoya Univ. | [Poster Presentation]
Training Algorithm for Restricted Boltzmann Machines Using Auxiliary Function Approach Norihiro Takamune (Univ. of Tokyo), Hirokazu Kameoka (Univ. of Tokyo/NTT) IBISML2014-56 |
Layerwise pre-training is one of important elements for deep learning, and Restricted Boltzmann Machines (RBMs) is popul... [more] | IBISML2014-56 pp.161-168 |
| 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 |
| SP, IPSJ-SLP (Joint) |
2014-07-25 10:20 |
Iwate | Hotel Hanamaki | [Invited Talk]
Voice conversion based on sparse representation and its application to articulation disorders Tetsuya Takiguchi (Kobe Univ.) SP2014-66 |
In recent years, approaches based on sparse representations have gained interest in a broad range of signal processing. ... [more] | SP2014-66 pp.19-24 |
[Return to Top Page]