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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| QIT (2nd) |
2024-05-28 13:00 |
Ibaraki | AIST Tsukuba | [Poster Presentation]
Analysis of interaction networks of qubits and quantum feature maps in a quantum machine learning model Aoi Hayashi (SOKENDAI/OIST/NII), Akitada Sakurai, William J. Munro (OIST), Kae Nemoto (OIST/NII) |
In this presentation, we introduce our investigations about the relation between learning performance of a quantum machi... [more] | |
| CQ, CS (Joint) |
2024-05-16 16:20 |
Aichi | (Primary: On-site, Secondary: Online) |
[Special Invited Talk]
Understanding and Mathematical Modeling of Human Behavior Takeshi Kurashima (NTT)  CS2024-2 CQ2024-9 |
The research field of "Behavioral Data Science," which deals with digitized human behavioral data using computational a... [more] | CS2024-2 CQ2024-9 p.3(CS), p.32(CQ) |
| DC, CPSY, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC [detail] | 2024-03-23 11:45 |
Nagasaki | Ikinoshima Hall (Primary: On-site, Secondary: Online) |
Evaluating composition of quantum circuit and learnability in quantum neural network with NISQ devices Naoki Marumo (Waseda Univ.), Yasutaka Wada (Meisei Univ.), Kazunori Ueda, Keiji Kimura (Waseda Univ.) CPSY2023-52 DC2023-118 |
The more numbers of repeat of Ansatz and the more qubit entangling improve learnability of quantum machine learning by v... [more] | CPSY2023-52 DC2023-118 pp.82-87 |
| CAS, CS | 2024-03-14 16:20 |
Okinawa | An Approximate Solution Using K-Shortest Path and Reinforcement Learning for a Load Balancing Problem in Communication Networks Himeno Takahashi, Norihiko Shinomiya (Soka Univ.) CAS2023-123 CS2023-116 |
In recent years, the amount of data traffic in information and communication networks has been increasing and the risk o... [more] | CAS2023-123 CS2023-116 pp.70-73 |
|
| CAS, CS | 2024-03-15 14:45 |
Okinawa | Exploring Uniform Convergence in Neural Networks and its Implication on Generalization Error Zong Xianzhe, Hiroshi Tamura (CHUO Univ.) CAS2023-135 CS2023-128 |
Uniform Convergence, a well-established framework for evaluating generalization in traditional Machine Learning, frequen... [more] | CAS2023-135 CS2023-128 pp.134-139 |
|
| ET | 2024-01-20 15:55 |
Kyoto | Kyoto University Yoshida Campus / Online (Primary: On-site, Secondary: Online) |
Difficulty-Controllable Question Generation of Reading Comprehension incorporating Answerability Evaluation Mechanism Ayaka Suzuki, Masaki Uto (UEC) ET2023-51 |
Question generation (QG) for reading comprehension is a technology for automatically generating questions related to giv... [more] | ET2023-51 pp.38-44 |
| HCGSYMPO (2nd) |
2023-12-11 - 2023-12-13 |
Fukuoka | Asia pacific Import Mart (Kitakyushu) (Primary: On-site, Secondary: Online) |
Deep Item Response Theory using Facial Features Yan Zhou, Kenji Suzuki (Univ. Tsukuba), Shiro Kumano (NTT/Univ. Tsukuba) |
This study proposes a method to integrate students' facial features during item responses into a deep item response theo... [more] | |
| ET | 2023-11-11 09:10 |
Kagawa | Kagawa University Saiwai-cho (Main) Campus / Online (Primary: On-site, Secondary: Online) |
Joint Generation of Questions and Reference Answers for Reading Comprehension with Difficulty-Controllability Teruyoshi Goto, Yuto Tomikawa, Masaki Uto (UEC) ET2023-24 |
Recently, deep learning techniques have been employed to automatically generate reading comprehension questions tailored... [more] | ET2023-24 pp.1-7 |
| MSS, CAS, SIP, VLD | 2023-07-07 11:20 |
Hokkaido | (Primary: On-site, Secondary: Online) |
Enhancing Glycan Recognition Pattern Learning with Tree-Structured Data Mining Kento Totsuka, Norihiko Shinomiya, Kiyoko Kinoshita, Masae Hosoda (Soka Univ.) CAS2023-20 VLD2023-20 SIP2023-36 MSS2023-20 |
In recent times, the rapid proliferation of semi-structured data has been primarily fueled by the emergence of the World... [more] | CAS2023-20 VLD2023-20 SIP2023-36 MSS2023-20 pp.97-101 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2023-06-30 11:10 |
Okinawa | OIST Conference Center (Primary: On-site, Secondary: Online) |
On performance degradation of a method by minimizing the conditional mutual information for the out-of-distribution generalization Genki Takahashi, Toshiyuki Tanaka (Kyoto University) NC2023-15 IBISML2023-15 |
In the out-of-distribution generalization problem, the smaller the degree of change in the data generating distribution ... [more] | NC2023-15 IBISML2023-15 pp.91-97 |
| IMQ, IE, MVE, CQ (Joint) [detail] |
2023-03-17 13:40 |
Okinawa | Okinawaken Seinenkaikan (Naha-shi) (Primary: On-site, Secondary: Online) |
A Consideration on Improving Frame Prediction Accuracy Using GAN and PredNet Naoko Imai, Shunichi Sekiguchi, Wataru Kameyama (Waseda Univ) IMQ2022-80 IE2022-157 MVE2022-110 |
We have been studying to apply PredNet, which is proposed to prove the predictive coding theory of human brain, to motio... [more] | IMQ2022-80 IE2022-157 MVE2022-110 pp.309-314 |
| CAS, CS | 2023-03-01 09:30 |
Fukuoka | Kitakyushu International Conference Center (Primary: On-site, Secondary: Online) |
Modification and Comparison of Learning Sudoku with Convolutional Networks Koichiro Ishii, Hiroshi Tamura (Chuo Univ.) CAS2022-96 CS2022-73 |
Sudoku is a logic puzzle in which the number between 1 and 9 is to fill in the boxes of the 9x9 grid by using the number... [more] | CAS2022-96 CS2022-73 pp.1-5 |
| IE | 2023-02-02 16:15 |
Tokyo | NII (Primary: On-site, Secondary: Online) |
[Invited Talk]
When Compressive Light Field Acquisition Meets Deep Learning Keita Takahashi (Nagoya Univ.) IE2022-56 |
The light field is a basic representation for 3-D visual information, and it is usually treated as a set of images taken... [more] | IE2022-56 p.20 |
| ET | 2023-01-20 15:15 |
Hyogo | Hyogo College of Medicine and Online (Primary: On-site, Secondary: Online) |
Multi-task Training with Joining-in-type Robot-assisted Language Learning System Yu Zha, Tsuneo Kato, Seiichi Yamamoto, Akihiro Tamura (Doshisha Univ.) ET2022-60 |
Introducing robots into language learning systems is effective, especially in motivating learners to engage in learning ... [more] | ET2022-60 pp.23-28 |
| IN | 2023-01-19 10:50 |
Aichi | Aichi Industry & Labor Center (Primary: On-site, Secondary: Online) |
A Time Series Analysis of Queueing Systems by Using Machine Learning Tomoyasu Kudo, Takashi Okuda (Aichi Prefectural Univ.) IN2022-54 |
The digital transformation's (DX) market has been expanding rapidly in recent years, and data processing systems that an... [more] | IN2022-54 pp.13-18 |
| AI | 2022-09-16 13:30 |
Shizuoka | (Primary: On-site, Secondary: Online) |
Temporal Modeling of Players for Multi-agent Coordination in Non-Cooperative Game Junjie Zhong, Toshiharu Sugawara (Waseda Univ.) AI2022-26 |
Multi-agent interaction structures that contain mixed cooperative-competitive relationships appear in many realistic sit... [more] | AI2022-26 pp.48-53 |
| IT | 2022-07-22 15:05 |
Okayama | Okayama University of Science (Primary: On-site, Secondary: Online) |
Bayes Optimal Approximation Algorithm by Boosting-like Construction of Meta-Tree Sets in Classification on Decision Tree Model Ryota Maniwa, Naoki Ichijo, Koshi Shimada, Toshiyasu Matsushima (Waseda Univ.) IT2022-28 |
Decision trees are used for classification and regression such as predicting the objective variable corresponding to the... [more] | IT2022-28 pp.67-72 |
| NS, SR, RCS, SeMI, RCC (Joint) |
2022-07-15 10:40 |
Ishikawa | The Kanazawa Theatre + Online (Primary: On-site, Secondary: Online) |
Proposal of a location correction method for the development of an activity history tracing system in hospital facilities using BLE beacons Shoki Kishizoe, Norihiko Shinomiya (Soka Univ) SeMI2022-39 |
This study aims to develop a system to obtain tracking information on persons in a building by analyzing data obtained f... [more] | SeMI2022-39 pp.83-86 |
| CCS, NLP | 2022-06-10 10:55 |
Osaka | (Primary: On-site, Secondary: Online) |
[Invited Talk]
Computation and learning based on dual stochasticity of the brain Jun-nosuke Teramae (Kyoto Univ.) NLP2022-16 CCS2022-16 |
Neurons and synapses in the brain are highly stochastic devices. Neurons responsible for signal propagation in the brain... [more] | NLP2022-16 CCS2022-16 pp.78-83 |
| MSS, NLP | 2022-03-28 13:00 |
Online | Online | A Study on Applying Game Balancing Method Using Deep Reinforcement Learning to Pokemon Ryohei Okamura, Atsuo Ozaki (OIT) MSS2021-61 NLP2021-132 |
We propose a method to automatically adjust the game balance of video games using rein-forcement learning. The problem w... [more] | MSS2021-61 NLP2021-132 pp.33-36 |
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