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All Technical Committee Conferences (Searched in: Recent 10 Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NLP, MSS | 2025-03-14 15:15 |
Okinawa | Miyakojima City Central Community Center | New Possibilities for Associative Memory in Neural Networks: Verification Using a Large-Scale Model of the Saito-Jinno Learning Method Kenya Jin'no (Tokyo City Univ.)  MSS2024-106 NLP2024-147 |
The Saito-Jin'no learning method is a learning method aimed at improving the performance of associative memory in neural... [more] | MSS2024-106 NLP2024-147 pp.199-202 |
| NC, NLP (Joint) |
2025-01-28 10:25 |
Osaka | Periodic orbits and stability in sparse binary neural networks Ryota Toyama, Hiroki Nonaka, Toshimichi saito (HU) NLP2024-93 |
Sparse binary neural networks are discrete-time recurrent neural networks consisting of majority decision neurons from t... [more] | NLP2024-93 pp.7-10 |
|
| NC, NLP (Joint) |
2025-01-29 09:30 |
Osaka | The Neural Network Model of Cerebellum-Cerebrum connection Taishiro Chiba, Kajimoto Shotaro, Hirokazu Tanaka (TCU) NC2024-50 |
In this lecture, we propose a computational principle suggesting that the cerebellum predicts and monitors cerebral cort... [more] | NC2024-50 pp.52-55 |
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| NC, NLP (Joint) |
2025-01-29 13:40 |
Osaka | Learning and generation of long-period rhythms using reservoir computing with slow features Yuji Kawai (Osaka Univ.), Shinya Fujii (Keio Univ.), Minoru Asada (IPUT/Osaka Univ./NICT/Chubu Univ.) NC2024-54 |
Reservoir computing (RC) has been utilized for learning and generating diverse time-series data, including musical seque... [more] | NC2024-54 pp.72-73 |
|
| MSS, SS | 2025-01-12 16:40 |
Kagoshima | [Invited Talk]
Exploring the Role of System and Control in the IoT/AI Era Ryosuke Adachi (Yamaguchi Univ.) MSS2024-54 SS2024-33 |
With the rapid spread of IoT, social systems such as electric power systems, transportation systems, and urban systems b... [more] | MSS2024-54 SS2024-33 p.65 |
|
| EMT, IEE-EMT | 2024-11-28 10:25 |
Shizuoka | Shizuoka Convestion & Arts Center | Proposal of Extended Liquid Time-Constant Neural Networks to Solve Partial Differential Equations Justin Jun Wilkins, Yukihisa Suzuki (Tokyo Metropolitan Univ.) EMT2024-77 |
The purpose of this study is to develop a machine learning model using deep learning as a surrogate model for analysing ... [more] | EMT2024-77 pp.96-101 |
| PN | 2024-11-18 13:55 |
Oita | (Primary: On-site, Secondary: Online) |
Sequence Estimation Method to Alleviate Spectrum Narrowing Reiji Higuchi, Taisei Sekizuka, Kazato Satake, Takuma Kuno (Nagoya Univ.), Yojiro Mori (Toyota Tech. Inst.), Hiroshi Hasegawa (Nagoya Univ.) PN2024-29 |
To efficiently increase network capacity, it is crucial to accelerate signal rates per wavelength and densify wavelength... [more] | PN2024-29 pp.12-16 |
| IBISML, NC, IPSJ-BIO, IPSJ-MPS [detail] | 2024-06-22 11:15 |
Okinawa | OIST | Oscillation-driven reservoir computing for long-term timing/chaotic time-series prediction Yuji Kawai (Osaka Univ.), Takashi Morita (Chubu Univ.), Park Jihoon (NICT/Osaka Univ.), Minoru Asada (IPUT/Osaka Univ./NICT/Chubu Univ.) NC2024-30 IBISML2024-30 |
Reservoir computing has been exploited for model-free prediction of various time series, including chaotic dynamical sys... [more] | NC2024-30 IBISML2024-30 pp.182-187 |
| CCS | 2024-03-27 14:00 |
Hokkaido | RUSUTSU RESORT | Evaluation of recurrent neural network training using multi-phase quantization optimizer Hiiro Yamazaki, Itsuki Akeno, Koki Nobori, Tetsuya Asai, Kota Ando (Hokkaido Univ.) CCS2023-44 |
In this research, we apply "Holmes", an optimizer dedicated to edge training of neural networks, to recurrent neural net... [more] | CCS2023-44 pp.30-35 |
| EMM | 2024-03-02 16:20 |
Overseas | Day1:JEJU TECHNOPARK, Day2:JEJU Business Agency | [Fellow Memorial Lecture]
Application of associative memory models to watermarking models Masaki Kawamura (Yamaguchi Univ.) EMM2023-93 |
We proposed a new method called the associative watermarking method, which is an extension of the zero-watermarking meth... [more] | EMM2023-93 pp.23-27 |
| NC, MBE (Joint) |
2023-10-27 14:45 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
Adaptive motion generation for a redundant robot arm using an echo state network Hiroshi Atsuta, Yuji Kawai (Osaka Univ.), Minoru Asada (IPUT/Osaka Univ./Chubu Univ./NICT) NC2023-28 |
Teaching playback is a convenient method to instruct robots how to move. However, this method has an issue of excessive ... [more] | NC2023-28 pp.17-22 |
| NC, MBE (Joint) |
2023-10-28 11:35 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
Lightweight modular reservoir computing with the Bernoulli weight distribution Yuji Kawai (Osaka Univ.), Minoru Asada (IPUT/Osaka Univ./Chubu Univ./NICT) NC2023-31 |
A modular reservoir computing, namely reservoir of basal dynamics (reBASICS), has been proposed for time series learning... [more] | NC2023-31 pp.25-30 |
| SIS, ITE-BCT | 2023-10-13 09:30 |
Yamaguchi | HISTORIA UBE (Primary: On-site, Secondary: Online) |
[Invited Talk]
In-sensor Material reservoir computing devices including tactile computing devices for robot applications Hirofumi Tanaka (Kyutech) SIS2023-20 |
Reservoir arithmetic elements, which are expected to be used as lighter-task AI hardware systems, were fabricated in ran... [more] | SIS2023-20 pp.25-28 |
| CCS, NLP | 2023-06-09 10:45 |
Tokyo | Tokyo City Univ. | Analysis and synthesis of dynamic binary neural networks: a short review Toshimichi Saito, Mikito Onuki (HU) NLP2023-20 CCS2023-8 |
Discrete-time recurrent neural networks are dynamical systems characterized by nonlinear activation functions and connec... [more] | NLP2023-20 CCS2023-8 pp.31-34 |
| AP | 2023-05-12 10:00 |
Okinawa | Okinawa Gender Equality Center (Primary: On-site, Secondary: Online) |
A Study on Radio Propagation Modeling using RNN-Encoder with Variable-Size Map Data Tatsuya Nagao, Takahiro Hayashi (KDDI Research) AP2023-17 |
For efficient evaluation of the performance of wireless systems in physical space, wireless emulation techniques in virt... [more] | AP2023-17 pp.48-53 |
| NC, MBE (Joint) |
2023-03-14 09:30 |
Tokyo | The Univ. of Electro-Communications (Primary: On-site, Secondary: Online) |
Dynamical analysis of concurrent and sequential multi-task learning in recurrent neural networks using the graph reduction method Daichi Sugiyama, Hiroki Kurashige (Tokai Univ.) NC2022-99 |
Humans and animals can learn multiple tasks. It is common that learning to learn tasks sequentially. However, it is not ... [more] | NC2022-99 pp.42-47 |
| SP, IPSJ-SLP, EA, SIP [detail] | 2023-03-01 09:30 |
Okinawa | (Primary: On-site, Secondary: Online) |
A Study on Scheduled Sampling for Neural Transducer-based ASR Takafumi Moriya, Takanori Ashihara, Hiroshi Sato, Kohei Matsuura, Tomohiro Tanaka, Ryo Masumura (NTT) EA2022-100 SIP2022-144 SP2022-64 |
In this paper, we propose scheduled sampling approaches suited for the recurrent neural network-transducer (RNNT) that i... [more] | EA2022-100 SIP2022-144 SP2022-64 pp.147-152 |
| R | 2022-10-07 14:50 |
Fukuoka | (Primary: On-site, Secondary: Online) |
A Note on A Transformer Encoder-Based Malware Classification Using API Calls Chen Li (Kyutech), Junjun Zheng (Osaka Univ.) R2022-36 |
Malware is a major security threat to computer systems and significantly impacts system reliability. Recurrent neural ne... [more] | R2022-36 pp.25-30 |
| SS, IPSJ-SE, KBSE [detail] | 2022-07-29 13:25 |
Hokkaido | Hokkaido-Jichiro-Kaikan (Sapporo) (Primary: On-site, Secondary: Online) |
Fault Localization for RNNs Based on Probabilistic Automata and n-grams Yuta Ishimoto, Masanari Kondo, Naoyasu Ubayashi, Yasutaka Kamei (Kyushu Univ.) SS2022-10 KBSE2022-20 |
If deep learning models misbehave, serious accidents may occur.Previous studies have proposed approaches to overcome suc... [more] | SS2022-10 KBSE2022-20 pp.55-60 |
| IT | 2022-07-22 10:50 |
Okayama | Okayama University of Science (Primary: On-site, Secondary: Online) |
A study on deep learning-based cyber attack detection Ruei-Fong Hong, Qiangfu Zhao (UoA), Shih-Cheng Horng (CYUT) IT2022-22 |
Cyberattack is a broad term for cybercrime that includes any deliberate attack on a computer device, network or infrastr... [more] | IT2022-22 pp.36-41 |
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