Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
SIP, SP, EA, IPSJ-SLP [detail] |
2024-02-29 16:20 |
Okinawa |
(Primary: On-site, Secondary: Online) |
EA2023-77 SIP2023-124 SP2023-59 |
In this paper, we consider a dynamic sensor placement problem where sensors can move within a network over time. Sensor ... [more] |
EA2023-77 SIP2023-124 SP2023-59 pp.97-102 |
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 |
QIT (2nd) |
2023-12-17 17:30 |
Okinawa |
OIST (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Sparse identification of quantum dynamics via quantum circuit learning Yusei Tateyama, Yuzuru Kato (FUN) |
Sparse Identification of Nonlinear Dynamics (SINDy) is a data-driven method for estimation and prediction of nonlinear d... [more] |
|
CPSY, IPSJ-ARC, IPSJ-HPC |
2023-12-06 17:15 |
Okinawa |
Okinawa Industry Support Center (Primary: On-site, Secondary: Online) |
An Efficient Sparse Matrix Storage Format for Sparse Matrix-Vector Multiplication and Sparse Matrix-Transpose-Vector Multiplication on GPUs Ryohei Izawa, Yasushi Inoguchi (JAIST) CPSY2023-37 |
The utilization of sparse matrix storage formats is widespread across various fields, including scientific computing, ma... [more] |
CPSY2023-37 pp.58-63 |
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 |
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 |
MI |
2023-03-07 15:25 |
Okinawa |
OKINAWA SEINENKAIKAN (Primary: On-site, Secondary: Online) |
[Short Paper]
An artifact reduction technique for sparse-view CT using frame interpolation Takayuki Okamoto, Hideaki Haneishi (Chiba Univ.) MI2022-120 |
Sparse-view CT is an imaging technique that reconstructs images by reducing the number of projection data. Although spar... [more] |
MI2022-120 pp.190-191 |
SIS |
2023-03-02 11:00 |
Chiba |
Chiba Institute of Technology (Primary: On-site, Secondary: Online) |
Blink detection from one-dimensional face signal by using convolutional sparse dictionary learning Souichiro Maruyama, Makoto Nakashizuka (CIT) SIS2022-40 |
In this report, a blink detection method from average intensities of whole facial videos using convolutional dictionary... [more] |
SIS2022-40 pp.1-4 |
ICTSSL |
2022-07-29 13:55 |
Osaka |
Kansai University (Primary: On-site, Secondary: Online) |
Behavior Analysis of Evacuees using Sparse Structure Learning for Development of Emergency Rescue Evacuation Support System Yeboon Yun, Tomotaka Wada (Kansai Univ.) ICTSSL2022-13 |
We have developed the Emergency Rescue Evacuation Support System (ERESS) which is designed to automatically detect disas... [more] |
ICTSSL2022-13 pp.16-21 |
MSS, NLP |
2022-03-29 09:40 |
Online |
Online |
Effects of sparse connections in spiking neural networks for unsupervised pattern recognition Hiroki Shinagawa, Kantaro Fujiwara, Gouhei Tanaka (Univ. of Tokyo) MSS2021-69 NLP2021-140 |
Recently, the spiking neural network (SNN) models, which compute using spatio-temporal information representation by neu... [more] |
MSS2021-69 NLP2021-140 pp.71-76 |
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 |
MBE, NC (Joint) |
2022-03-04 14:20 |
Online |
Online |
Investigation of machine learning methods for emotion discrimination by using phase synchronization of electroencephalogram Fumiya Hirooka, Jiro Okuda (Kyoto Sangyo Univ. Grad. Sch.) NC2021-74 |
This study investigated machine learning methods for emotion discrimination by using phase synchronization of electroenc... [more] |
NC2021-74 pp.143-148 |
IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] |
2022-02-21 12:45 |
Online |
Online |
Quality Assessment for 3D CG Image Colorization Using Visible Digital Watermarking after Noise Removal Based on Sparse Dictionary Learning Coding Norifumi Kawabata (Hokkaido Univ.) |
Thus far, we discussed to represent image data whether it is possible or not to represent meaning image how requirement ... [more] |
|
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 |
PRMU |
2021-08-26 10:00 |
Online |
Online |
Unsupervised non-rigid alignment for multiple noisy images Takanori Asanomi, Kazuya Nishimura, Heon Song, Junya Hayashida (Kyushu Univ.), Hiroyuki Sekiguchi (Kyoto Univ.), Takayuki Yagi (Luxonus), Imari Sato (NII), Ryoma Bise (Kyushu Univ.) PRMU2021-7 |
We propose a deep non-rigid alignment network that can simultaneously perform non-rigid alignment and noise decompositio... [more] |
PRMU2021-7 pp.1-6 |
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 |
CPSY, DC, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC [detail] |
2021-03-25 14:40 |
Online |
Online |
Parallelization and Vectorization of SpMM for Sparse Neural Network Yuta Tadokoro, Keiji Kimura, Hironori Kasahara (Waseda Univ.) CPSY2020-55 DC2020-85 |
Pruning is one of the well-known model compression techniques in Deep Learning. Eliminating less important weights in th... [more] |
CPSY2020-55 DC2020-85 pp.31-36 |
MI |
2021-03-17 13:30 |
Online |
Online |
Noise reduction method for sparse-view computed tomography using spatial frequency components Takayuki Okamoto, Hideaki Haneishi (Chiba Univ.) MI2020-95 |
Sparse-view CT, an imaging technique to reduce the number of projections, can reduce the radiation dose and scanning dur... [more] |
MI2020-95 pp.207-211 |
HWS, VLD [detail] |
2021-03-03 13:00 |
Online |
Online |
[Memorial Lecture]
Scheduling Sparse Matrix-Vector Multiplication onto Parallel Communication Architecture Mingfei Yu, Ruitao Gao, Masahiro Fujita (Univ. Tokyo) VLD2020-71 HWS2020-46 |
There is an obvious trend to make use of hardware including many-core CPU, GPU and FPGA, to conduct computationally inte... [more] |
VLD2020-71 HWS2020-46 pp.24-29 |