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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NC, MBE, NLP, MICT (Joint) [detail] |
2024-01-25 16:00 |
Tokushima | Naruto University of Education | Design of an Efficient Activity Classification Model Focusing on the Characteristics of Egocentric Videos Kohei Baba, Kantaro Fujiwara (University of Tokyo), Gouhei Tanaka (Nagoya Institute of Technology)  NLP2023-116 MICT2023-71 MBE2023-62 |
There are situations where egocentric videos have to be processed on wearable devices with limited computational resourc... [more] | NLP2023-116 MICT2023-71 MBE2023-62 pp.153-157 |
| NC, NLP | 2023-01-29 15:55 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Indoor air quality prediction using multi-reservoir echo state network with attention mechanism Wenrui Qiu, Gouhei Tanaka (UTokyo) NLP2022-106 NC2022-90 |
Indoor air quality (IAQ) is a critical matter of concern in terms of its impact on public health and well-being. Researc... [more] | NLP2022-106 NC2022-90 pp.135-140 |
| CCS, NLP | 2022-06-09 15:45 |
Osaka | (Primary: On-site, Secondary: Online) |
Reservoir computing with spiking neural networks and reward-modulated STDP Takayuki Tsurumi, Gouhei Tanaka (UTokyo) NLP2022-7 CCS2022-7 |
In a previous study, it was verified that tasks requiring nonlinearity and working memory can be performed using reward-... [more] | NLP2022-7 CCS2022-7 pp.31-35 |
| 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 |
| MSS, NLP | 2022-03-29 10:05 |
Online | Online | Relationship between Computational Performance and Task Difficulty of Reinforcement Learning Methods Using Reward Machines Ryuji Watanabe, Gouhei Tanaka (The Univ. of Tokyo) MSS2021-70 NLP2021-141 |
In reinforcement learning, it is necessary to take into account the history of past state transitions during learning fo... [more] | MSS2021-70 NLP2021-141 pp.77-82 |
| MBE, NC (Joint) |
2022-03-02 09:55 |
Online | Online | NC2021-47 |
In this paper, we propose reconstructive reservoir computing (RRC), which can detect anomaly in time-series signals. In ... [more] | NC2021-47 pp.5-10 |
| MBE, NC (Joint) |
2022-03-02 15:20 |
Online | Online | NC2021-56 |
We propose a spin-wave antenna structure that penetrates a garnet film, which we named film-penetrating transducers (FPT... [more] | NC2021-56 pp.50-55 |
| NC, MBE (Joint) |
2019-03-05 10:20 |
Tokyo | University of Electro Communications | Reservoir computing devices that realize pattern information representation and processing directly in physics: Its advantages Akira Hirose, Gouhei Tanaka (Tokyo Univ.), Seiji Takeda, Toshiyuki Yamane, Hidetoshi Numata, Naoki Kanazawa, JeanBenoit Heroux, Daiju Nakano (IBM Japan), Ryosho Nakane (Tokyo Univ.) NC2018-65 |
[more] | NC2018-65 pp.117-120 |
| CCS | 2018-11-22 16:00 |
Hyogo | Kobe Univ. | [Invited Talk]
Dynamical robustness of complex networks Gouhei Tanaka (Univ. Tokyo) CCS2018-39 |
There are many systems regarded as complex networks in the real world, including engineering networks such as communicat... [more] | CCS2018-39 pp.33-38 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Nonlinear Time Series Prediction using Multi-Step Learning Echo State Networks Takanori Akiyama, Gouhei Tanaka (Tokyo Univ.) IBISML2018-83 |
Reservoir Computing (RC) has recently attracted much attention as brain-like information processing for high-speed learn... [more] | IBISML2018-83 pp.293-299 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
3D-Skeleton-Based Human Action Recognition with a Combination of Random Convolutional Networks and Echo State Networks Zhiqiang Tong, Gouhei Tanaka (Univ. of Tokyo) IBISML2018-99 |
Recognition of human action patterns from sequential data has been intensively studied in recent years. Although many ma... [more] | IBISML2018-99 pp.411-417 |
| NLP | 2017-07-14 10:00 |
Okinawa | Miyako Island Marine Terminal | Cascading failure in power network models under large-scale blackouts Yuto Takizawa, Gouhei Tanaka (Univ. of Tokyo) NLP2017-39 |
(To be available after the conference date) [more] | NLP2017-39 pp.63-66 |
| IBISML | 2014-11-18 15:00 |
Aichi | Nagoya Univ. | [Poster Presentation]
Performance analysis of auto-associative neural networks on diluted modular networks Toshiyuki Yamane (TRL), Gouhei Tanaka (Univ. Tokyo), Daiju Nakano (TRL), Ryosho Nakane (Univ. Tokyo), Yasunao Katayama (TRL) IBISML2014-82 |
We report the performance analysis of auto-associative neural networks on modular structures. We focus on the sparse mo... [more] |
IBISML2014-82 pp.351-356 |
| NLP | 2009-11-14 11:25 |
Kagoshima | Numerical Analysis on Coupled Systems of Period-1 and Period-2 Limit Cycle Oscillators Yusuke Okada, Gouhei Tanaka, Takashi Kohno, Kazuyuki Aihara (Univ. of Tokyo) NLP2009-118 |
Coupled oscillators have been widely studied, for instance, as a mathematical model to investigate the mechanism of emer... [more] | NLP2009-118 pp.203-208 |
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| NLP | 2008-02-01 10:45 |
Hokkaido | Complex-Valued Multistate Associative Memory with Nonlinear Multilevel Function Gouhei Tanaka, Kazuyuki Aihara (Tokyo Univ.) NLP2007-144 |
A complex-valued neural network can be used for multistate associative memory by quantizing a neuronal state into a mult... [more] | NLP2007-144 pp.13-18 |
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