Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
NC, MBE (Joint) |
2023-03-15 10:30 |
Tokyo |
The Univ. of Electro-Communications (Primary: On-site, Secondary: Online) |
Proposal of Node Fusion in Sparse DenseNet Shogo Taneda, Shoma Noguchi, Yukari Yamauchi (Nihon Univ.) NC2022-110 |
Gao Huang et al. proposed a deep learning model called DenseNet. This deep learning model successfully prevents informat... [more] |
NC2022-110 pp.105-108 |
IBISML |
2022-09-15 15:05 |
Kanagawa |
Keio Univ. (Yagami Campus) (Primary: On-site, Secondary: Online) |
Improving Efficiency of Regularization Path Computation in Safe Pattern Pruning via Multiple Referential Solutions Takumi Yoshida (Nitech), Hiroyuki Hanada (RIKEN), Kazuya Nakagawa, Shinya Suzumura, Onur Boyar, Kazuki Iwata (Nitech), Shun Shimura, Yuji Tanaka (NaogyaU), Masayuki Karasuyama (Nitech), Kouichi Taji (NaogyaU), Koji Tsuda (UTokyo/RIKEN), Ichiro Takeuchi (NaogyaU/RIKEN) IBISML2022-38 |
Safe Screening and Safe Pattern Pruning are methods for efficiently modeling high-dimensional features by $L_1$-regulari... [more] |
IBISML2022-38 pp.39-46 |
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] |
2020-06-29 13:50 |
Online |
Online |
Performance comparison of autoencoders and sparse PCAs Masumi Ishikawa (Kyutech) NC2020-4 IBISML2020-4 |
Principal component analysis (PCA) is an effective tool for clarifying data structure. Each principal component includes... [more] |
NC2020-4 IBISML2020-4 pp.21-26 |
IT, ISEC, WBS |
2019-03-08 10:15 |
Tokyo |
University of Electro-Communications |
Typical performance of the L1 regularization regression from linear measurements with measurement noise and large coherence Minori Ihara, Kazunori Iwata, Kazushi Mimura (Hiroshima City Univ.) IT2018-117 ISEC2018-123 WBS2018-118 |
We evaluate typical performance of compressed sensing in the case where iterative recovery algorithms fail to converge. ... [more] |
IT2018-117 ISEC2018-123 WBS2018-118 pp.257-262 |
NC, MBE (Joint) |
2019-03-04 17:00 |
Tokyo |
University of Electro Communications |
Towards understandable deep learning in stacked autoencoders Masumi Ishikawa (Kyutech) NC2018-62 |
Recent progress of deep learning(DP) is remarkable and its recognition ability is said to surpass that of humans. The ac... [more] |
NC2018-62 pp.99-104 |
RCS, SIP, IT |
2019-01-31 10:15 |
Osaka |
Osaka University |
A Study on Regularization Parameter in OFDM Communication Using Sparse Channel Estimation Kenta Kawahara, Takahiro Natori (Tokyo Univ. of Science), Takashi Yoshida (TMCIT), Akira Nakamura, Makoto Itami, Naoyuki Aikawa (Tokyo Univ. of Science) IT2018-38 SIP2018-68 RCS2018-245 |
In recent years, sparse estimation using signal sparsity, which is one solution to the inverse problem, attracts attenti... [more] |
IT2018-38 SIP2018-68 RCS2018-245 pp.19-24 |
RCS, SIP, IT |
2019-02-01 15:35 |
Osaka |
Osaka University |
A study of physical layer security using L1 regularized channel estimation techniques Yasuhiro Takano (Kove Univ.) IT2018-69 SIP2018-99 RCS2018-276 |
An L1 regularized channel estimation technique can, even with a short training sequence (TS), achieve estimation perform... [more] |
IT2018-69 SIP2018-99 RCS2018-276 pp.197-201 |
IBISML |
2017-11-10 13:00 |
Tokyo |
Univ. of Tokyo |
[Poster Presentation]
Proposal of λ-scan Method in Spectral Deconvolution Yohachi Mototake (Univ of Tokyo), Yasuhiko Igarashi (NIMS), Hikaru Takenaka (Univ of Tokyo), Kenji Nagata (AIST), Masato Okada (Univ of Tokyo) IBISML2017-80 |
Spectral deconvolution is a method to fit spectral data as the sum of unimodal basis functions and is a useful method in... [more] |
IBISML2017-80 pp.325-332 |
IBISML |
2017-11-10 13:00 |
Tokyo |
Univ. of Tokyo |
[Poster Presentation]
Effect of maximum likelihood estimation after L1 regularization in learning of log-linear models Kazuya Takabatake, Shotaro Akaho (AIST) IBISML2017-86 |
$L_1$ regularization has two functions.
One function is the structure learning by parameter reduction, and another func... [more] |
IBISML2017-86 pp.369-375 |
WBS, MICT |
2017-07-13 13:45 |
Shizuoka |
ACT CITY |
[Poster Presentation]
Performance evaluation of blind time-variant channel estimation using L1 regularization for OFDM systems Naoto Murakami, Teruyuki Miyajima (Ibaraki Univ.) WBS2017-8 MICT2017-10 |
In this article, we propose a blind channel estimation method for time-variant channels in OFDM transmission. The propos... [more] |
WBS2017-8 MICT2017-10 pp.1-6 |
IT, SIP, RCS |
2017-01-20 11:15 |
Osaka |
Osaka City Univ. |
Performance of L1 regularized channel estimation techniques using information criteria Yasuhiro Takano (Kobe Univ.) IT2016-85 SIP2016-123 RCS2016-275 |
Most $ell1$ regularized channel estimation techniques assume that degree of sparsity (DoS) is known. The variance of cha... [more] |
IT2016-85 SIP2016-123 RCS2016-275 pp.227-230 |
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] |
2016-07-05 16:25 |
Okinawa |
Okinawa Institute of Science and Technology |
Sparse Simultaneous Estimation of Phase Response Curves using Spike-Triggered Average Man Arakaki, Yasuhiko Igarashi (Univ. Tokyo), Toshiaki Omori (Kobe Univ.), Masato Okada (Univ. Tokyo/RIKEN) NC2016-7 |
Phase response curves (PRC) have been used extensively to estimate the response properties of neurons.
A recent measure... [more] |
NC2016-7 pp.165-170 |
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] |
2016-07-06 11:15 |
Okinawa |
Okinawa Institute of Science and Technology |
Sparse cross-domain matching correlation analysis via L1 regularization Keisuke Kojima (Osaka Univ.), Kei Hirose (Kyushu Univ.), Hidetoshi Shimodaira (Osaka Univ.) IBISML2016-5 |
(To be available after the conference date) [more] |
IBISML2016-5 pp.209-215 |
IA |
2016-01-29 13:30 |
Tokyo |
Kikai-Shinko-Kaikan Bldg. B3 Kenshu-2 room |
Proposal and evaluation of cooperative sensing method for device power reduction Akinori Iwakawa, Jun Kakuta (Fujitsu Labs.) IA2015-82 |
Researching and developing of IOT solution becomes popular. In IOT solution, power consumption of each sensors are sensi... [more] |
IA2015-82 pp.19-24 |
RCS, IT, SIP |
2016-01-18 10:10 |
Osaka |
Kwansei Gakuin Univ. Osaka Umeda Campus |
Effectiveness of L1 Regularization for Sparse Impulse Response Estimation Using colored Noise Keito Kito, Ryo Tanaka, Takahiro Murakami (Meiji Univ.) IT2015-58 SIP2015-72 RCS2015-290 |
In this paper, we show an effectiveness of L1 regularization for a sparse impulse response estimation using a colored no... [more] |
IT2015-58 SIP2015-72 RCS2015-290 pp.61-66 |
MI |
2015-03-03 10:10 |
Okinawa |
Hotel Miyahira |
TOF-MRA image reconstruction by compressed sensing technique
-- comparison of three different regularization techniques -- Akira Yamamoto, Koji Fujimoto, Yasutaka Fushimi, Tomohisa Okada, Kei Sano, Toshiyuki Tanaka, Kaori Togashi (Kyoto Univ.) MI2014-93 |
Three different regularization terms, L1-norm of signal intensity, L1-norm of wavelet coefficients, and total variation,... [more] |
MI2014-93 pp.189-192 |
RCC, ASN, NS, RCS, SR (Joint) |
2014-07-30 17:00 |
Kyoto |
Kyoto Terrsa |
On numerical computation of sparse optimal control Takuya Ikeda, Masaaki Nagahara (Kyoto Univ.) RCC2014-25 |
In this article, we consider sparse optimal control with L2 regularization for the states. Under the normality assumptio... [more] |
RCC2014-25 pp.19-22 |
CAS, SIP, MSS, VLD, SIS [detail] |
2014-07-11 18:10 |
Hokkaido |
Hokkaido University |
High quality recovery of nonsparse signals from compressed sensing Aiko Nishiyama, Yuki Yamanaka, Akira Hirabayashi (Ritsumeikan Univ.), Kazushi Mimura (Hiroshima City Univ.) CAS2014-24 VLD2014-33 SIP2014-45 MSS2014-24 SIS2014-24 |
We propose a novel algorithm for the recovery of non-sparse, but compressible signals from linear undersampled measureme... [more] |
CAS2014-24 VLD2014-33 SIP2014-45 MSS2014-24 SIS2014-24 pp.123-127 |
NC, MBE (Joint) |
2012-12-12 16:05 |
Aichi |
Toyohashi University of Technology |
Sparse Estimation of Spike-Triggered Average Shimpei Yotsukura (Univ. of Tokyo), Toshiaki Omori (Kobe Univ.), Kenji Nagata, Masato Okada (Univ. of Tokyo) NC2012-87 |
Spike triggered average (STA) and phase response curve characterize response properties of single neurons. A recent theo... [more] |
NC2012-87 pp.61-66 |