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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 46  /  [Next]  
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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