Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
SIP, IT, RCS |
2024-01-18 11:45 |
Miyagi |
(Primary: On-site, Secondary: Online) |
A Study on Massive MIMO Channel Estimation Based on Sparse Bayesian Learning Using Hierarchical Model Kengo Furuta, Takumi Takahashi, Kenta Ito (Osaka Univ.), Shinsuke Ibi (Doshisha Uni.) IT2023-34 SIP2023-67 RCS2023-209 |
Massive multi-input multi-output (MIMO) channels are known to have pseudo-sparsity in the angular (beam) domain, and it ... [more] |
IT2023-34 SIP2023-67 RCS2023-209 pp.25-30 |
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] |
2023-06-29 16:50 |
Okinawa |
OIST Conference Center (Primary: On-site, Secondary: Online) |
Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers Yasutoshi Ida, Sekitoshi Kanai, Kazuki Adachi, Atsutoshi Kumagai, Yasuhiro Fujiwara (NTT) NC2023-8 IBISML2023-8 |
When we use discrete optimal transport (OT) for unsupervised domain adaptation, a group-sparse regularizer is frequently... [more] |
NC2023-8 IBISML2023-8 pp.48-55 |
BioX, SIP, IE, ITE-IST, ITE-ME [detail] |
2023-05-19 10:30 |
Mie |
Sansui Hall, Mie University (Primary: On-site, Secondary: Online) |
Privacy Preserving Deep Unrolling Methods using Random Unitary Transform Nichika Yuge, Takayuki Nakachi, Morikazu Nakamura (Univ. of the Ryukyus.) SIP2023-10 BioX2023-10 IE2023-10 |
Edge and cloud computing has been spreading in many fields including machine learning.Sparse modeling attracts attention... [more] |
SIP2023-10 BioX2023-10 IE2023-10 pp.41-46 |
EST |
2023-01-26 16:00 |
Okinawa |
(Primary: On-site, Secondary: Online) |
Estimation of magnetic dipole positions using sparse modeling Tomonori Yanagida, Yuji Ogata, Bunichi Kakinuma, Masayuki Kimishima (Advantest Lab) EST2022-87 |
In recent years, magnetic fields have attracted attention as applications for non-contact, non-destructive measurement o... [more] |
EST2022-87 pp.70-73 |
IBISML |
2022-12-22 13:40 |
Kyoto |
Kyoto University (Primary: On-site, Secondary: Online) |
IBISML2022-43 |
In recent years, materials science fields have been conducting efficient materials development through informatics-in th... [more] |
IBISML2022-43 pp.4-5 |
MBE, NC (Joint) |
2022-03-02 11:00 |
Online |
Online |
Learning of a stacked autoencoder with regularizers added to the cost function, evaluation of their effectiveness, and clarification of its information compression mechanism Masumi Ishikawa (Kyutech) NC2021-49 |
Deep learning has a serious drawback in that the resulting models tend to be a black box, hence hard to understand. A sp... [more] |
NC2021-49 pp.17-22 |
NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-23 12:10 |
Online |
Online |
Deep learning of mixture of continuous and categorical data with regularizers added to the cost function and evaluation of the effectiveness of sparse modeling Masumi Ishikawa (Kyutech) NC2021-45 |
Deep learning has a serious drawback in that the resulting models tend to be a black box, hence hard to understand. A sp... [more] |
NC2021-45 pp.65-70 |
RISING (3rd) |
2021-11-17 09:00 |
Tokyo |
(Primary: On-site, Secondary: Online) |
Interference Estimation Method Using Sparse Modeling Based on Spectrum Database Hiroki Ito (UEC), Kei Inage (TMCIT), Takeo Fujii (UEC) |
With the Internet of Things (IoT) era, the number of devices that share the same frequency is rapidly increasing. IoT de... [more] |
|
IA, ICSS |
2021-06-22 11:15 |
Online |
Online |
A Solution for Recovering Missing Links in Network Topology using Sparse Modeling Ryotaro Matsuo, Hiroyuki Ohsaki (Kwansei Gakuin Univ.) IA2021-14 ICSS2021-14 |
In recent years, sparse modeling, which is a statistical approach, has been applied to many practical problems mostly in... [more] |
IA2021-14 ICSS2021-14 pp.74-79 |
EMM, IT |
2021-05-20 16:10 |
Online |
Online |
[Invited Talk]
Secure Computation of Sparse Modeling
-- Edge AI with Lightweight and Small Amounts of Data -- Takayuki Nakachi (Univ. of the Ryukyus) IT2021-6 EMM2021-6 |
With the advent of the big data, IoT, AI era, all digital contents continue to increase. Sparse modeling is drawing atte... [more] |
IT2021-6 EMM2021-6 pp.31-36 |
NC, MBE (Joint) |
2021-03-03 13:00 |
Online |
Online |
Hybrid Sparsity in Convolutional Neural Networks Shoma Noguchi, Yukari Yamauchi (Nihon Univ.) NC2020-46 |
Convolutional neural networks (CNNs) have achieved high accuracy in areas such as image classification and object detect... [more] |
NC2020-46 pp.21-24 |
SeMI, IPSJ-MBL, IPSJ-UBI [detail] |
2021-03-02 14:30 |
Online |
Online |
[Poster Presentation]
Wireless channel characterization with sparse modeling Naota Takeyama, Jin Mitsugi (Keio Univ.) SeMI2020-66 |
In wireless channel characterization using adaptive filters, adaptive algorithms based on Lasso, Group Lasso, and SCAD h... [more] |
SeMI2020-66 pp.47-56 |
IA |
2020-10-01 13:15 |
Online |
Online |
A Study on Recovering Network Topology with Missing Links using Sparse Modeling Ryotaro Matsuo, Hiroyuki Ohsaki (Kwansei Gakuin Univ.) IA2020-3 |
In recent years, sparse modeling, which is a statistical approach, has been applied to many practical problems mostly in... [more] |
IA2020-3 pp.10-13 |
MI, IE, SIP, BioX, ITE-IST, ITE-ME [detail] |
2020-05-28 10:50 |
Online |
Online |
[Special Talk]
High-dimensional Signal Restoration by Convolutional Networks Driving Fusion Across Multiple Disciplines
-- Sparse Modeling and Convolutional Dictionary Learning -- Shogo Muramatsu (Niigata Univ.) |
This talk outlines a restoration process of high-dimensional signals such as image and volumetric data. With the develop... [more] |
|
NC, MBE (Joint) |
2020-03-06 16:10 |
Tokyo |
University of Electro Communications (Cancelled but technical report was issued) |
Sparse modeling of deep classification networks with layer-wise greedy learning and various regularization terms Masumi Ishikawa (Kyutech) NC2019-116 |
Training of deep networks is difficult due to vanishing gradients. To overcome this difficulty, layer-wise greedy learni... [more] |
NC2019-116 pp.231-236 |
SP, EA, SIP |
2020-03-02 15:10 |
Okinawa |
Okinawa Industry Support Center (Cancelled but technical report was issued) |
A Pattern Recognition Method Using Secure Sparse Representations in L0 Norm Minimization Takayuki Nakachi, Yitu Wang (NTT), Hitoshi Kiya (Tokyo Metro. Univ.) EA2019-130 SIP2019-132 SP2019-79 |
In this paper, we propose a privacy-preserving pattern recognition method using encrypted sparse representations in L0 n... [more] |
EA2019-130 SIP2019-132 SP2019-79 pp.169-174 |
IT, SIP, RCS |
2020-01-24 13:00 |
Hiroshima |
Hiroshima City Youth Center |
Proposal of Denoising Method Based on Sparseness of NMRS Signals Satoru Kubota (TUS), Kazunori Uruma (Kogakuin), Yuuho Tanaka, Norisato Suga, Toshihiro Furukawa (TUS) IT2019-74 SIP2019-87 RCS2019-304 |
Nuclear magnetic resonance spectroscopy (NMRS) is very useful in basic chemical and physiological research, including th... [more] |
IT2019-74 SIP2019-87 RCS2019-304 pp.221-225 |
NC, MBE |
2019-12-06 15:40 |
Aichi |
Toyohashi Tech |
Prevention of redundant representations and of the black box in stacked autoencoders Masumi Ishikawa (Kyutech) MBE2019-56 NC2019-47 |
Recent progress in deep learning (DL) is remarkable and its recognition capability is said to surpass that of humans. Th... [more] |
MBE2019-56 NC2019-47 pp.67-72 |
IE, CS, IPSJ-AVM, ITE-BCT [detail] |
2019-12-05 11:40 |
Iwate |
Aiina Center |
[Special Talk]
Representation of moving-image's sparsity and its applications to adaptive moving-image restoration Takahiro Saito (Kanagawa Univ.) CS2019-75 IE2019-55 |
This talk states that statistical sparsity of a moving-image sequence can be properly represented in the domain of the 3... [more] |
CS2019-75 IE2019-55 pp.29-34 |
MIKA (2nd) |
2019-10-03 13:35 |
Hokkaido |
Hokkaido Univ. |
[Invited Lecture]
DOA Estimation Using Sparse Modeling Toshihiko Nishimura, Seigi Nakatsu, Takeo Ohgane, Yasutaka Ogawa (Hokkaido Univ.) |
The problem of estimating the direction of arrival (DOA) of radio waves from signals received by multiple antennas is a ... [more] |
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