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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SIP, IT, RCS | 2024-01-19 10:20 |
Miyagi | (Primary: On-site, Secondary: Online) |
Coordinated Multi-Point with Hierarchical Active Inference of Position and Propagation Channel Tatsuya Otoshi, Masayuki Murata (Osaka Univ.)  IT2023-63 SIP2023-96 RCS2023-238 |
This study focuses on cooperative beamforming among base stations in wireless communication technology and proposes a ne... [more] | IT2023-63 SIP2023-96 RCS2023-238 pp.181-186 |
| RISING (3rd) |
2023-10-31 09:45 |
Hokkaido | Kaderu 2・7 (Sapporo) | [Poster Presentation]
Study of Localization Accuracy and Communication Performance of IRS-Assisted ISAC Systems Mihiro Hashimoto, Koji Yamamoto, Itsuki Yonemura (Kyoto Univ.), Toshiro Nakahira, Daisuke Murayama, Takuto Arai, Daisei Uchida, Naoki Kita (NTT) |
This study experimentally demonstrates a device-free localization in intelligent reflecting surface (IRS)-assisted milli... [more] | |
| RISING (3rd) |
2023-10-31 13:00 |
Hokkaido | Kaderu 2・7 (Sapporo) | [Poster Presentation]
Object Position Prediction Leveraging Both-side Angular Power Profiles in mmWave Communications Itsuki Yonemura (Kyoto Univ.), Koji Yamamoto (KIT), Kazunori Hayashi (Kyoto Univ.), Takuto Arai, Shuki Wai, Tatsuhiko Iwakuni, Daisei Uchida, Naoki Kita (NTT) |
This study experimentally demonstrates a novel position prediction leveraging both-side angular power profiles (APPs) sc... [more] | |
| MI | 2023-07-03 10:00 |
Miyagi | Tohoku Univ. Sakura Hall | HCP data processing by hybrid approach for parameter inference in free water imaging model of diffusion MRI
-- comparison of diffusion tensor computation methods for datasets without b0 image -- Yoshitaka Masutani (Tohoku Univ.), Keigo Yamazaki, Wataru Uchida, Koji Kamagata (Juntendo Univ.), Koh Sasaki (HHC), Shigeki Aoki (Juntendo Univ.) MI2023-7 |
We have proposed a hybrid approach combining synthetic Q-space learning and conventional fitting for parameter estimatio... [more] | MI2023-7 pp.1-2 |
| PRMU, IPSJ-CVIM | 2023-05-19 09:35 |
Aichi | (Primary: On-site, Secondary: Online) |
Free-viewpoint Omnidirectional Image Synthesis for Large Scenes Fuma Yasue, Kazutoshi Akita, Norimichi Ukita (TTI) PRMU2023-8 |
Recently, Neural Radiance Fields (NeRF) have been attracting attention as a method for arbitrary viewpoint image generat... [more] | PRMU2023-8 pp.39-44 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-03 17:10 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Forest Construction of Gaussian and Discrete Variables based on WBIC Ashraful Islam, Joe Suzuki (Osaka Univ.) PRMU2022-126 IBISML2022-133 |
Mutual information is a metric that determines the association between two random variables by measuring the amount of i... [more] | PRMU2022-126 IBISML2022-133 pp.371-377 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-03 17:25 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
The free energy of the mean field approximation in normal crossing functions Miki Aoyagi (Nihon Univ.) PRMU2022-127 IBISML2022-134 |
Then main terms of the free energy in singular models are obtained by a resolution map of Kullback function's singularit... [more] | PRMU2022-127 IBISML2022-134 pp.378-383 |
| NC, MBE (Joint) |
2022-09-29 13:50 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
Isotropic Inference and Modularity in Neural Networks Yuma Onishi, Tomoaki Imajo, Yusuke Matsubara, Koiti Hasida (UTokyo) NC2022-37 |
Neural networks can be regarded as a system of simultaneous equations. Reasoning with it is to solve the simultaneous eq... [more] | NC2022-37 pp.20-23 |
| NS, IN (Joint) |
2022-03-11 14:50 |
Online | Online | Enhancing Resilience of an MQTT Broker Using Immunity-Based Attack Detection
-- Detection and Prevention of DoS Attacks against Use-After-Free Vulnerabilities -- Takeshi Okamoto (KAIT) NS2021-158 |
Smartification of public services is being promoted, but smart public services are at risk of cyberattacks. One of cyber... [more] | NS2021-158 pp.212-217 |
| PRMU, IPSJ-CVIM | 2022-03-11 10:15 |
Online | Online | Context free grammar with tree structure analysis for online handwritten mathematical expression recognition Naruki Kitashima, Cuong Tuan Nguyen, Ikuko Shimizu, Masaki Nakagawa (TUAT) PRMU2021-76 |
In mathematics exams, descriptive questions are effective for measuring the comprehension of examinees, but marking hand... [more] | PRMU2021-76 pp.103-108 |
| NC, MBE (Joint) |
2020-03-04 09:55 |
Tokyo | University of Electro Communications (Cancelled but technical report was issued) |
Inverted pendulum control with redundancy by freezing model using deep reinforcement learning Koki Hirakawa, Naohiro Fukumura (Toyohashi Univ. of Tech) NC2019-76 |
In recent years, various intelligent robots have been researched and developed, and multi-degree-of-freedom robots that ... [more] | NC2019-76 pp.3-8 |
| NC, MBE (Joint) |
2020-03-05 13:00 |
Tokyo | University of Electro Communications (Cancelled but technical report was issued) |
Bayesian learning curve for the case when the optimal distribution is not unique Shuya Nagayasu, Sumio Watanabe (Tokyo Tech) NC2019-94 |
Bayesian inference is a widely used statistical method. Asymptotic behaviors of generalization loss and free energy in B... [more] | NC2019-94 pp.107-112 |
| IBISML | 2020-01-09 13:00 |
Tokyo | ISM | Asymptotic Behavior of Bayesian Generalization Error in Multinomial Mixtures Takumi Watanabe, Sumio Watanabe (Tokyo Tech) IBISML2019-18 |
Multinomial mixtures are widely used in the information engineering field. However, it is not subject to the conventiona... [more] | IBISML2019-18 pp.1-8 |
| IBISML | 2020-01-09 13:25 |
Tokyo | ISM | Real Log Canonical Threshold of Three Layered Neural Network with Swish Activation Function Raiki Tanaka, Sumio Watanabe (Tokyo Tech) IBISML2019-19 |
In neural network learning, it is known that selection of activation function effects generalization performance. Althou... [more] | IBISML2019-19 pp.9-15 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Variational Approximation Accuracy in Non-negative Matrix Factorization Naoki Hayashi (MSI) IBISML2018-51 |
The asymptotic behavior of the variational free energy of the non-negative matrix factorization (NMF) has been elucidate... [more] | IBISML2018-51 pp.53-60 |
| TL | 2018-07-28 16:40 |
Tokyo | Keio University | [Poster Presentation]
(Most) frequently observed grammatical errors of Japanese EFL learners even after the six years of English learning Toshiyuki Yamada (Gunma Univ.) TL2018-25 |
This preliminary study investigates what grammatical errors are most frequently observed in Japanese EFL (English as a F... [more] | TL2018-25 pp.65-70 |
| CCS | 2017-11-10 13:30 |
Osaka | Osaka Univ. | [Invited Lecture]
Cerebellar circuits for learning from limited training samples Isao Tokuda (Ritsumei), Huu Hoang, Mitsuo Kawato (ATR) CCS2017-30 |
The cerebellum plays a pivotal role in motor learning. Despite a huge amount of degrees-of-freedom involved in movements... [more] | CCS2017-30 pp.49-53 |
| PN | 2017-03-06 17:05 |
Okinawa | Ohama Nobumoto Memorial Hall (Okinawa) | [Invited Talk]
Artificial Intelligence has already defeated human being Shigeki Goto (Waseda Univ./MBA) PN2016-99 |
It is said that Artificial Intelligence (AI) will eventually exceed the capacity of Human Being (Singularity). The autho... [more] | PN2016-99 pp.91-92 |
| IBISML | 2016-11-17 14:00 |
Kyoto | Kyoto Univ. | Extraction of Cluster Structural Changes using Variational Bayes Daisuke Kaji (Denso), Kazuho Watanabe (Toyohashi Tech.) IBISML2016-78 |
Variational Bayes learning (VB) is widely applied to clustering problems as the low computational cost algorithm of Baye... [more] | IBISML2016-78 pp.229-233 |
| PRMU, IPSJ-CVIM, IBISML [detail] | 2016-09-06 11:15 |
Toyama | Hyper-parameter Optimization with Derivative-free Method Yoshihiko Ozaki, Masaki Yano (Univ. Tsukuba/AIST), Masaki Onishi (AIST), Takahito Kuno (Univ. Tsukuba) PRMU2016-84 IBISML2016-39 |
In machine learning methods, an appropriate hyper-parameter tuning is really important for classifiers to perform its be... [more] | PRMU2016-84 IBISML2016-39 pp.227-232 |
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