| 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 # | |
| WPT | 2016-06-03 15:20 |
Tokyo | Tokyo University | BOLT AXIAL FORCE MEASURE AND MANAGEMENT SYSTEM USING WIRELESS POWER TRANSFER Fumitaka Saigo (Sannohashi), Ryo Sakadume, Tomohiro Yamato, Yasutoshi Suzaki (Toyo Electronics), Shunsuke Takahashi (Waseda Univ.)  WPT2016-13 |
Using wireless power transfer technology, the system of bolt axial force measuring device with one-touch handy terminal ... [more] | WPT2016-13 pp.19-24 |
| 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 |
| US | 2015-08-24 14:40 |
Tokyo | Tokyo Institute of Technology, Ookayama Campus | A structure of piezoelectric transformer for high power applications Kohei Suzuki, Kazunari Adachi, Yuki Shibamata (Yamagata Univ.) US2015-42 |
We propose a piezoelectric transformer comprising two identical Bolt-clamped Langevin-type transducers (BLTs) and a step... [more] | US2015-42 pp.25-30 |
| 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 |
| NLP | 2014-07-01 10:00 |
Miyagi | Tohoku Univ. | Learning Restricted Boltzmann Machine with discrete learning parameter Seitaro Shinagawa (Tohoku Univ.), Yoshihiro Hayakawa (SNCT), Shigeo Sato, Takeshi Onomi, Koji Nakajima (Tohoku Univ.) NLP2014-27 |
Recently, the method of Deep Neural Network (DNN) with hierarchical learning has been remarkable for performance to solv... [more] | NLP2014-27 pp.37-40 |
| SP, IPSJ-MUS | 2014-05-25 11:30 |
Tokyo | A joint restricted Boltzmann machine for dictionary learning in sparse-representation-based voice conversion Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) SP2014-34 |
In voice conversion, sparse-representation-based methods have recently been garnering attention because they are, relati... [more] | SP2014-34 pp.343-348 |
|
| SP, IPSJ-SLP | 2013-12-19 17:15 |
Tokyo | Speaker-dependent conditional restricted Boltzmann machine for voice conversion Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) SP2013-88 |
In this paper, we present a voice conversion (VC) method that utilizes conditional restricted Boltzmann machines (CRBMs)... [more] | SP2013-88 pp.83-88 |
|
| SP, IPSJ-SLP | 2013-12-20 10:45 |
Tokyo | [Invited Talk]
Acoustic Modeling Using Restricted Boltzmann Machines and Deep Belief Networks for Statistical Parametric Speech Synthesis and Voice Conversion Zhen-Hua Ling, Ling-Hui Chen, Li-Rong Dai (USTC) SP2013-90 |
This paper summarizes our previous work on spectral modeling using restricted Boltzmann machines (RBM) and deep belief n... [more] | SP2013-90 pp.103-108 |
|
| 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 |
| MI | 2013-07-19 12:50 |
Miyagi | [Special Talk]
Learning Features by Deep Networks and Its Application to Image Recognition Takayuki Okatani (Tohoku Univ) MI2013-33 |
Deep learning is a methodology in machine learning that uses multi-layered neural networks. It have gained a lot of atte... [more] | MI2013-33 p.69 |
|
| NLP | 2013-05-27 17:05 |
Fukuoka | Event hall, Central Library, Fukuoka University | A phenomenological model of turning motion of a nut attached to a vibrationally-stimulated bolt
-- Ballistic motion and chaotic diffusion in a two-dimensional potential with a periodic external field -- Hirotaka Tominaga (Saga Univ.), Syuji Miyazaki (Kyoto Univ.) NLP2013-17 |
Bolted construction on a shake table gets a screw loose. A nut attached to a bolt in immediate contact with a vibrating... [more] |
NLP2013-17 pp.43-46 |
[Return to Top Page]