Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
IN, CCS (Joint) |
2022-08-04 14:00 |
Hokkaido |
Hokkaido University(Centennial Hall) (Primary: On-site, Secondary: Online) |
On Learning of MLP by PSO with Perturbation Riku Takatou, Kenya Jin'no (Tokyo City Univ.) CCS2022-32 |
Attempt to learn the coupling coefficients of a multiclass classifier using a simplified multilayer perceptron (MLP) wit... [more] |
CCS2022-32 pp.30-34 |
RCS, SR, SRW (Joint) |
2022-03-02 10:25 |
Online |
Online |
Investigation on Beamforming for IRS-Assisted MIMO-OFDM Communication using Machine Learning Julian Webber, Kazuto Yano, Norisato Suga, Yoshinori Suzuki (ATR) SR2021-86 |
There has recently been considerable interest in intelligent reflective surface (IRS) which can improve the capacity of ... [more] |
SR2021-86 pp.6-13 |
IN, IA (Joint) |
2021-12-17 14:30 |
Hiroshima |
Higashi-Senda campus, Hiroshima Univ. (Primary: On-site, Secondary: Online) |
Evaluating Prediction of the Access Counts of Videos Using Metadata as a Learning Parameter Gen Koujitani, Masaya Nakayama (Univ. Tokyo) IA2021-47 |
In order to predict the number of accesses to videos posted on video sharing services, we performed regression predictio... [more] |
IA2021-47 pp.77-84 |
VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 10:10 |
Online |
Online |
A Multilayer Perceptron Training Accelerator using Systolic Array Takeshi Senoo, Akira Jinguji, Ryosuke Kuramochi, Hiroki Nakahara (Toyko Tech) VLD2021-23 ICD2021-33 DC2021-29 RECONF2021-31 |
Neural networks are being used in various applications, and the demand for fast training with large amounts of data is e... [more] |
VLD2021-23 ICD2021-33 DC2021-29 RECONF2021-31 pp.37-42 |
EST |
2020-01-30 10:30 |
Oita |
Beppu International Convention Center |
Capacitance Matrix Estimation of Multiconductor Transmission Lines Using Machine Learning Yuya Sato, Tadatoshi Sekine, Shin Usuki, Kenjiro T. Miura (Shizuoka Univ.) EST2019-82 |
In this report, we propose a technique that estimates capacitance matrices of multiconductor transmission lines (MTLs) b... [more] |
EST2019-82 pp.19-24 |
NLP |
2019-09-23 12:30 |
Kochi |
Eikokuji Campus, University of Kochi |
Optimization of Neural Network by Using Swarm Intelligence Takumi Nakamura, Gennki Yoshida, Chihiro Ikuta (NIT, Suzuka College) NLP2019-40 |
In this study, we investigate about a learning of multi-layer perceptron (MLP) by using firefly algorithm. The firefly a... [more] |
NLP2019-40 pp.27-30 |
EA, SIP, SP |
2019-03-14 13:30 |
Nagasaki |
i+Land nagasaki (Nagasaki-shi) |
[Poster Presentation]
Snore sound identification using noise suppression and multi-class classification under real environments Keisuke Nishijima, Ken'ichi Furuya (Oita Univ.) EA2018-106 SIP2018-112 SP2018-68 |
In the conventional snore sound identification method, there is an issue that performance deteriorates when identifying ... [more] |
EA2018-106 SIP2018-112 SP2018-68 pp.43-48 |
ICSS |
2018-11-21 14:50 |
Kagoshima |
|
Spoofed Website Detection using Machine Learning Naoki Kurihara, Hidenori Tsuji, Masaki Hashimoto (Institute of Information Security) ICSS2018-56 |
In recent years, the damage by fake site has been rapidly increasing. Because fake sites are ceremonious as if they are ... [more] |
ICSS2018-56 pp.19-24 |
IMQ |
2018-10-19 15:05 |
Kyoto |
Kyoto Institute of Technology |
Examination of dimensionality of multilayer perceptron estimating dislocation regions in multicrystalline silicon photoluminescence image Hiroaki Kudo, Tetsuya Matsumoto (Nagoya Univ.), Kentaro Kutsukake (RIKEN), Noritaka Usami (Nagoya Univ.) IMQ2018-14 |
In this report, we studied a specified method of regions including dislocations which are crystallographic defects in a ... [more] |
IMQ2018-14 pp.19-24 |
SC |
2018-06-02 09:25 |
Fukushima |
UBIC 3D Theater, University of Aizu |
Simultaneous recognition of human activities and locations based on sensor array Shoichi Ichimura, Qiangfu Zhao (Univ. of Aizu) SC2018-11 |
In recent years, smart homes for senior care have attracted great attention in Japan. But a smart home has a privacy iss... [more] |
SC2018-11 pp.59-64 |
SIP, EA, SP, MI (Joint) [detail] |
2018-03-19 13:00 |
Okinawa |
|
[Poster Presentation]
Automatic transcription of playing the shamisen by harmonic structure and positions on pressable string Keita Masaki, Hiroaki Kudo, Tetsuya Matsumoto, Noboru Ohnishi (Nagoya Univ.), Yoshinori Takeuchi (Daido Univ.) EA2017-117 SIP2017-126 SP2017-100 |
In this report, we propose a method to estimate onset times, pitches and sounding string of a shamisen performance in or... [more] |
EA2017-117 SIP2017-126 SP2017-100 pp.93-100 |
IBISML |
2017-11-10 13:00 |
Tokyo |
Univ. of Tokyo |
Analysis of Dropout in online learning Kazuyuki Hara (Nihon Univ.) IBISML2017-61 |
Deep learning is the state-of-the-art in fields such as visual object recognition and speech recognition.
This learning... [more] |
IBISML2017-61 pp.201-206 |
IBISML |
2017-11-10 13:00 |
Tokyo |
Univ. of Tokyo |
Statistical Mechanical Analysis of Learning with Two-Layer Perceptron with Multiple Output Units
-- Reconsidering Plateau Phenomenon -- Yuki Yoshida (UTokyo), Ryo Karakida (AIST), Masato Okada (UTokyo/AIST/RIKEN BSI), Shun-ichi Amari (RIKEN BSI) IBISML2017-83 |
The plateau phenomenon --- stopping decrease of error in the middle of learning --- is problematic.
Since Amari et al. ... [more] |
IBISML2017-83 pp.347-354 |
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] |
2017-06-23 16:00 |
Okinawa |
Okinawa Institute of Science and Technology |
Analysis of Robustness of Approximators Based on Neural Networks Against Redundant Dimensions Shoichi Someno, Tomohiro Tanno, Kazumasa Horie, Jun Izawa, Tomoki Ichiba, Masahiko Morita (Tsukuba Univ.) NC2017-8 |
Redundant input dimensions that are not related to the output are known to lower the approximate accuracy of function ap... [more] |
NC2017-8 pp.21-26 |
NLC, TL |
2016-06-04 16:50 |
Hokkaido |
Otaru University of Commerce |
Identification of Tweets that Mention Books
-- Effects of Features, Data Size, and ML Algorithms -- Shuntaro Yada, Kyo Kageura (UTokyo) TL2016-7 NLC2016-7 |
We report performances of a classifier that identify Tweets that Mention Books (TMB) from tweets that contain the same s... [more] |
TL2016-7 NLC2016-7 pp.29-34 |
NC, NLP (Joint) |
2016-01-29 15:25 |
Fukuoka |
Kyushu Institute of Technology |
Statistical Mechanics of Perceptron Learning with Noisy Teacher Arata Honda, Kazushi Ikeda (NAIST) NC2015-65 |
Learning curves of simple perceptron were derived here. They have been analyzed for half a century and the learning curv... [more] |
NC2015-65 pp.45-48 |
IBISML |
2015-11-27 14:00 |
Ibaraki |
Epochal Tsukuba |
[Poster Presentation]
Performance degradation of AMP for Ising perceptron when the system size is small Arise Kuriya, Toshiyuki Tanaka (Kyoto Univ.) IBISML2015-85 |
Approximate Massage Passing (AMP) algorithm, proposed by Donoho et al., is derived from Belief Propagation (BP) algorith... [more] |
IBISML2015-85 pp.241-247 |
NC, MBE |
2015-03-16 15:35 |
Tokyo |
Tamagawa University |
Further Speeding Up and Solution Quality Improvement of Singularity Stairs Following Seiya Satoh, Ryohei Nakano (Chubu Univ.) MBE2014-168 NC2014-119 |
In a search space of a multilayer perceptron (MLP), there exists singular regions where any point is I-O equivalent to t... [more] |
MBE2014-168 NC2014-119 pp.289-294 |
NC, MBE (Joint) |
2013-12-21 15:20 |
Gifu |
Gifu University |
Singularity Stairs Following with Limited Numbers of Hidden Units Seiya Satoh, Ryohei Nakano (Chubu Univ.) NC2013-65 |
In a search space of a multilayer perceptron having J hidden units, MLP(J), there exist flat areas called singular regio... [more] |
NC2013-65 pp.69-74 |
NC, NLP |
2013-01-24 09:30 |
Hokkaido |
Hokkaido University Centennial Memory Hall |
Multilayer Perceptron Search Making Good Use of Singular Regions Seiya Satoh, Ryohei Nakano (Chubu Univ.) NLP2012-104 NC2012-94 |
In a search space of multilayer perceptron having J hidden units, MLP(J), there exists a singular flat region created by... [more] |
NLP2012-104 NC2012-94 pp.1-6 |