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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SAT, RCS (Joint) |
2022-08-26 11:15 |
Hokkaido | (Primary: On-site, Secondary: Online) |
Transmit Power Control for MIMO Small Cells Networks Employing Neural Networks with Compressed Sensing Algorithms Haoxiang Luan, Yuyuan Chang, Kazuhiko Fukawa (Tokyo Tech)  RCS2022-117 |
In small cell networks, random deployment of small cell base stations (BSs) can cause overlapping coverage areas, which ... [more] | RCS2022-117 pp.114-119 |
| NS, SR, RCS, SeMI, RCC (Joint) |
2022-07-14 15:00 |
Ishikawa | The Kanazawa Theatre + Online (Primary: On-site, Secondary: Online) |
Network Diagnosis with Group Testing
-- Minimum Number of Measurements and Optimal Probes -- Fangyuan Xu, Shun-ichi Azuma, Ryo Ariizumi, Toru Asai (Nagoya Univ.) RCC2022-29 |
In a network system, some connection failures may occur over the links. The administrator has to detect such links as so... [more] | RCC2022-29 pp.54-56 |
| RCC, WBS, SAT, MICT | 2022-05-27 10:00 |
Online | Online | Sparse Event-triggered Control under Disturbance Ikumi Banno, Shun-ichi Azuma, Ryo Ariizumi, Toru Asai (Nagoya Univ.) WBS2022-12 RCC2022-12 SAT2022-12 MICT2022-12 |
In networked control, the situation where control input is restricted to be sparse often arises. In this context, a fram... [more] | WBS2022-12 RCC2022-12 SAT2022-12 MICT2022-12 pp.58-60 |
| IT, EMM | 2022-05-18 10:50 |
Gifu | Gifu University (Primary: On-site, Secondary: Online) |
Binarized Neural Networks and Trainable ISTA based Signature Code with Channel Estimation for Multiple Access Rayleigh Fading Lantian Wei, Shan Lu, Hiroshi Kamabe (Gifu Univ.) IT2022-10 EMM2022-10 |
User Identification (UI) and Channel Estimation (CE) schemes are essential issues in wireless networks with massive user... [more] | IT2022-10 EMM2022-10 pp.50-55 |
| 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 |
| ICTSSL, CAS | 2022-01-20 11:30 |
Online | Online | FPGA implementation and evaluation of Ternary sparse XNOR-Net and Proposal of Ternary sparse Net without XNOR Taichi Megumi, Takayuki Kawahara (Tokyo Univ of Science) CAS2021-55 ICTSSL2021-32 |
Ternary Sparse XNOR-Net is a method to suppress decline of recognition accuracy by ternarizing the weights of the neural... [more] | CAS2021-55 ICTSSL2021-32 pp.19-23 |
| RCS, SIP, IT | 2022-01-21 10:55 |
Online | Online | A lossless audio codec based on hierarchical residual prediction Taiyo Mineo, Shouno Hayaru (UEC) IT2021-71 SIP2021-79 RCS2021-239 |
In this study, we propose a novel lossless audio codec that has precise predictive performance from the neural network a... [more] | IT2021-71 SIP2021-79 RCS2021-239 pp.239-244 |
| IBISML | 2022-01-18 13:00 |
Online | Online | Local Explanation of Graph Neural Network through Predictive Graph Mining Hinata Asahi, Masayuki Karasuyama (NIT) IBISML2021-23 |
Graph Neural Networks (GNNs) have attracted wide attention in the data science community. However, predictions of GNNs a... [more] | IBISML2021-23 pp.37-44 |
| DC, CPSY, IPSJ-ARC [detail] | 2021-10-11 10:00 |
Online | Online | A Study for Accelerating SpMV Using FPGA with High Bandwidth Memory Ryosuke Yanagisawa, Kenji Kanazawa, Moritoshi Yasunaga (University of Tsukuba) CPSY2021-12 DC2021-12 |
Sparse Matrix-Vector Multiplication (SpMV) is a fundamental operation that appears in various computer science applicati... [more] | CPSY2021-12 DC2021-12 pp.1-6 |
| DE, IPSJ-DBS, IPSJ-IFAT | 2021-09-16 13:00 |
Online | Online | Ensemble BERT-BiLSTM-CNN Model for Sequence Classification Vuong Thi Hong (NII/SOKENDAI), Takasu Atsuhiro (NII) DE2021-12 |
Ensemble methods use multiple learning algorithms to obtain better predictive performance. Currently, deep learning mode... [more] | DE2021-12 pp.1-6 |
| 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 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2021-06-28 15:20 |
Online | Online | Predictive Graph Mining using Graphs with Interval Attributes Hinata Asahi, Masayuki Karasuyama (NIT) NC2021-6 IBISML2021-6 |
Graphs have been widely used to represent structured data such as molecular data and traffic networks. In this paper, we... [more] | NC2021-6 IBISML2021-6 pp.39-46 |
| 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 |
| 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 |
| 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 |
| 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 |
| NC, MBE (Joint) |
2021-03-04 14:10 |
Online | Online | What characteristics are acquired in coding self-motion from visual motion?
-- Reconstruction of statistical relationship by neural network and its internal representation -- Daiki Nakamura, Hiroaki Gomi (NTT) NC2020-57 |
Efficient coding is a prevailing computational models of sensory coding in the brain. If the sensory information is tran... [more] | NC2020-57 pp.83-88 |
| AI | 2021-02-22 14:40 |
Online | Online | A Study on the Application of Curriculum Learning in Deep Reinforcement Learning
-- action acquisition in shooting game AI as an example -- Ikumi Kodaka, Fumiaki Saito (CIT) AI2020-47 |
Deep reinforcement learning is attracting attention because it can be applied to higher-dimensional environments compare... [more] | AI2020-47 pp.47-52 |
| EST | 2021-01-21 11:15 |
Online | Online | Performance Evaluation of EEG Source Localization Based on Lead Field Matrix Takayoshi Moridera, Essam Rashed, Akimasa Hirata (NITech) EST2020-57 |
In recent years, effective utilization of biosignals has been progressing in medical science and industrial applications... [more] | EST2020-57 pp.22-27 |
| CQ (2nd) |
2021-01-21 13:30 |
Online | Online | [Invited Lecture]
Individual representation of creativity using large-scale brain dataset Takeshi Ogawa (ATR) |
Thanks to development of machine learning methods based on large-scale data, it has made huge impacts on not only image ... [more] | |
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