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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| 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 |
| 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 | 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 |
| 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 |
|
| IBISML | 2012-11-07 15:30 |
Tokyo | Bunkyo School Building, Tokyo Campus, Tsukuba Univ. | Regularization of Restricted Boltzmann Machine Learning through entropy minimization Taichi Kiwaki, Takaki Makino, Kazuyuki Aihara (Univ. Tokyo) IBISML2012-48 |
We propose a learning scheme for Restricted Boltzmann Machines (RBMs) that suppresses over-fitting, where the entropy of... [more] | IBISML2012-48 pp.103-106 |
| 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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