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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| RCC, ISEC, IT, WBS | 2023-03-14 13:50 |
Yamaguchi | (Primary: On-site, Secondary: Online) |
A Proposal of Defining Fields for Efficient Ring-LWE Based Cryptography Yuya Okubo, Shinya Okumura (Osaka Univ.), Atsuko Miyaji (Osaka Univ./JAIST)  IT2022-91 ISEC2022-70 WBS2022-88 RCC2022-88 |
Currently, as a post-quantum computer cryptography, cryptography based on the difficulty of solving a mathematical probl... [more] | IT2022-91 ISEC2022-70 WBS2022-88 RCC2022-88 pp.142-148 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2022-06-29 15:00 |
Okinawa | (Primary: On-site, Secondary: Online) |
Emergence of Dynamical Orthogonal Basis Acquiring Large Memory Capacity in Modular Reservoir Computing Yuji Kawai (Osaka Univ.), Jihoon Park (NICT/Osaka Univ.), Ichiro Tsuda (Chubu Univ.), Minoru Asada (IPUT/Osaka Univ./Chubu Univ./NICT) NC2022-28 IBISML2022-28 |
The brain's ability to generate complex spatiotemporal patterns with a specific timing is essential for motor learning a... [more] | NC2022-28 IBISML2022-28 pp.193-198 |
| ISEC, SITE, LOIS | 2019-11-01 14:00 |
Osaka | Osaka Univ. | Lattice basis reduction over projected lattices and its application to solving the LWE problem Satoshi Nakamura, Nariaki Tateiwa (Kyushu Univ.), Koha Kinjo (NTT), Yasuhiko Ikematsu, Masaya Yasuda (Kyushu Univ.) ISEC2019-68 SITE2019-62 LOIS2019-27 |
The security of modern lattice-based schemes is based on the computational hardness of solving the learning with errors ... [more] | ISEC2019-68 SITE2019-62 LOIS2019-27 pp.41-48 |
| EA, SP, SIP | 2016-03-29 13:15 |
Oita | Beppu International Convention Center B-ConPlaza | Effective basis learning for sound source separation by semi-supervised nonnegative matrix factorization Daichi Kitamura (SOKENDAI), Nobutaka Ono (NII/SOKENDAI), Hiroshi Saruwatari (UT), Yu Takahashi, Kazunobu Kondo (Yamaha) EA2015-130 SIP2015-179 SP2015-158 |
This paper addresses a sound source separation problem and proposes an effective basis learning method for semi-supervis... [more] | EA2015-130 SIP2015-179 SP2015-158 pp.355-360 |
| IBISML | 2014-11-18 15:00 |
Aichi | Nagoya Univ. | [Poster Presentation]
Basis functions for fast learning of log-linear models Kazuya Takabatake, Shotaro Akaho (AIST) IBISML2014-76 |
We propose basis functions for log-linear models and a fast learning algorithm that works on these bases. These bases a... [more] |
IBISML2014-76 pp.307-312 |
| NC, MBE (Joint) |
2012-12-12 10:40 |
Aichi | Toyohashi University of Technology | A numerical derivation of learning coefficient in radial basis function network Satoru Tokuda, Kenji Nagata, Masato Okada (Univ. of Tokyo) NC2012-78 |
Radial basis function (RBF) network is a regression model which regresses input-output data by radial basis functions su... [more] | NC2012-78 pp.25-30 |
| IBISML | 2012-11-08 15:00 |
Tokyo | Bunkyo School Building, Tokyo Campus, Tsukuba Univ. | An Ordinal Regression Model Based on Logistic Regression Models and Its Fast Sparse Bayesian Learning Kazuhisa Nagashima, Masato Inoue (Waseda Univ.) IBISML2012-87 |
The common solution to the ordinal regression problem uses the model in which noise-contained inputs are deterministical... [more] | IBISML2012-87 pp.381-385 |
| NC | 2010-10-23 11:55 |
Fukuoka | Kyushu Inst. Tech. (Kitakyushu Sci. and Res. Park) | A neural network model for multiple 3D object recognition Yasuaki Higuchi, Nobuhiko Asakura, Toshio Inui (Kyoto Univ.) NC2010-44 |
We propose a neural network model for recognition of multiple objects that extends the Generalized Radial Basis Function... [more] | NC2010-44 pp.11-16 |
| NC, MBE (Joint) |
2009-03-12 13:25 |
Tokyo | Tamagawa Univ. | Covariate Shift and Incremental Learning Koichiro Yamauchi (Hokudai Univ.) NC2008-142 |
Learning strategies under `covariate shift' have recently been widely discussed. Under covariate shift, the density o... [more] |
NC2008-142 pp.231-236 |
| MI | 2008-01-25 13:00 |
Okinawa | Naha-Bunka-Tenbusu | Liver Segmentation in 3D Abdominal CT Images Based on Maximum a Posterior Probability Method and Ensemble Learning Shinya Tanaka, Akinobu Shimizu, Daisuke Furukawa, Hidefumi Kobatake (TUAT), Shigeru Nawano (Center for Radiological Sciences, IUHW), Kenji Shinozaki (NKCC) MI2007-88 |
This paper describes improvements of the liver region segmentation algorithm using three phase abdominal 3D CT images , ... [more] | MI2007-88 pp.123-130 |
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