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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| 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 |
| SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] | 2022-05-19 13:00 |
Kumamoto | Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
[Special Talk]
Integrative technologies of mathematical modeling and deep Learning
-- Three strategies to distill inductive biases -- Tomoya Sakai (Nagasaki Univ.) SIP2022-9 BioX2022-9 IE2022-9 MI2022-9 |
(To be available after the conference date) [more] | SIP2022-9 BioX2022-9 IE2022-9 MI2022-9 pp.49-54 |
| 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 |
| NLC, IPSJ-NL, SP, IPSJ-SLP [detail] | 2021-12-02 14:20 |
Online | Online | End-to-End Speech Recognition System Using Sparse Representation Reiichiro Yasaki, Makoto Ohki (Yamanashi Univ.) NLC2021-20 SP2021-41 |
(To be available after the conference date) [more] | NLC2021-20 SP2021-41 pp.13-16 |
| 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] | |
| MI | 2021-07-08 14:00 |
Online | Online | Unsupervised deep learning with low-rank and sparse priors for blood vessel enhancement from free-breathing angiography Ryoji Ishibashi, Tomoya Sakai (Nagasaki Univ.), Hideaki Haneishi (Chiba Univ.) MI2021-11 |
(To be available after the conference date) [more] | MI2021-11 pp.11-14 |
| 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 |
| 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 |
| 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 |
| 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] | |
| NC, IBISML, IPSJ-MPS, IPSJ-BIO [detail] | 2019-06-17 15:00 |
Okinawa | Okinawa Institute of Science and Technology | A model selection criterion for LASSO estimate with scaling Katsuyuki Hagiwara (Mie Univ.) IBISML2019-5 |
To relax a bias problem in LASSO (Least Absolute Shrinkage and election Operator), there have been several studies inclu... [more] | IBISML2019-5 pp.27-34 |
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