Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
MI |
2024-03-03 09:41 |
Okinawa |
OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
A preliminary study on deep causal discovery model for image classification Ryohei Motoda, Megumi Nakao (Kyoto Univ.) MI2023-33 |
Although saliency map used in image classification can visualize the regions correlated with predicted class, it cannot ... [more] |
MI2023-33 pp.11-14 |
SIP, SP, EA, IPSJ-SLP [detail] |
2024-02-29 15:10 |
Okinawa |
(Primary: On-site, Secondary: Online) |
Lightweight and Interpretable Deep Learning Model for EEG-Based Sleep Stage Classification Aozora Ito, Toshihisa Tanaka (TUAT) EA2023-82 SIP2023-129 SP2023-64 |
Sleep scoring by experts is necessary for diagnosing sleep disorders. EEG is one of the essential physiological data for... [more] |
EA2023-82 SIP2023-129 SP2023-64 pp.127-132 |
IBISML |
2022-09-15 14:00 |
Kanagawa |
Keio Univ. (Yagami Campus) (Primary: On-site, Secondary: Online) |
Interpretable Model Combining statements and DNN Ryo Okuda, Yuya Yoshikawa (STAIR) IBISML2022-36 |
In this study, we propose a method that achieves both interpretability of Decision Tree and the prediction accuracy of D... [more] |
IBISML2022-36 pp.25-30 |
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] |
2022-06-27 17:25 |
Okinawa |
(Primary: On-site, Secondary: Online) |
Additive Cumulative Link Model with Total Variation Regularization Hiroya Iyori, Shin Matsushima (Univ. of Tokyo) NC2022-8 IBISML2022-8 |
In many fields such as medical research and social science, data on an ordinal scale are often obtained.
Problems in wh... [more] |
NC2022-8 IBISML2022-8 pp.69-75 |
SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] |
2022-05-20 16:20 |
Kumamoto |
Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
Visualization of Important Features for Classifier Decisions using Deep Image Synthesis Yushi Haku, Megumi Nakao, Tetsuya Matsuda (Kyoto Univ.) SIP2022-28 BioX2022-28 IE2022-28 MI2022-28 |
It is difficult to know the basis for the decisions of machine learning models, and it is necessary to provide a highly ... [more] |
SIP2022-28 BioX2022-28 IE2022-28 MI2022-28 pp.144-149 |
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 |
MI |
2021-03-15 13:45 |
Online |
Online |
Surgical planning model generation by extracting important feature sets in mandibular reconstruction Kazuki Nagai, Megumi Nakao (Kyoto Univ.), Nobuhiro Ueda (Nara Medical Univ.), Yuichiro Imai (Rakuwakai Otowa Hospital), Toshihide Hatanaka, Tadaaki Kirita (Nara Medical Univ.), Tetsuya Matsuda (Kyoto Univ.) MI2020-54 |
Because implicit medical knowledge and experience are used to perform medical treatment, such decisions must be clarifie... [more] |
MI2020-54 pp.29-34 |
MI |
2021-03-15 14:00 |
Online |
Online |
Analysis of important features in surgical planning for mandibular reconstruction among multiple surgeons Yusuke Hatakeyama, Kazuki Nagai, Megumi Nakao, Tetsuya Matsuda (Kyoto Univ.) MI2020-55 |
Surgeons perform surgical treatment by considering the facilities and policies of medical institutions and their own exp... [more] |
MI2020-55 pp.35-40 |
SIS |
2020-12-01 14:25 |
Online |
Online |
Interpretability of deep neural networks with self-organizing map modules. Takahiro Sono, Keiichi Horio (KIT) SIS2020-32 |
In recent years, the technology of neural networks has made great progress due to the improvement of computational power... [more] |
SIS2020-32 pp.27-30 |
RISING (2nd) |
2019-11-27 13:55 |
Tokyo |
Fukutake Learning Theater, Hongo Campus, Univ. Tokyo |
[Poster Presentation]
A Study on Interpretation of Review Classification by SVM and DNN Kosuke Nakamura, Saneyasu Yamaguchi (Kogakuin Univ.) |
Deep learning has achieved significant improvement in various tasks such as natural language processing. However, it is ... [more] |
|
VLD, DC, CPSY, RECONF, ICD, IE, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC (Joint) [detail] |
2019-11-14 16:35 |
Ehime |
Ehime Prefecture Gender Equality Center |
Domain Knowledge-aware Machine Learning System with Rule-based Guiding Tomoaki Shikina, Daichi Teruya, Hironori Nakajo (TAT) CPSY2019-44 |
Data-driven methods in machine learning rely only on the statistical nature of the data. Therefore, its predictions coul... [more] |
CPSY2019-44 pp.23-28 |
PRMU, BioX |
2019-03-17 14:45 |
Tokyo |
|
A Study of Business Interpretation Technique for AI Predictions Naoaki Yokoi, Masashi Egi (Hitachi, Ltd.) BioX2018-39 PRMU2018-143 |
(To be available after the conference date) [more] |
BioX2018-39 PRMU2018-143 pp.61-66 |
AI |
2018-12-07 14:40 |
Fukuoka |
|
AI2018-28 |
Many researches targeting review of goods and services are doing today. Although research is conducted from various view... [more] |
AI2018-28 pp.15-18 |
IBISML |
2018-11-05 15:10 |
Hokkaido |
Hokkaido Citizens Activites Center (Kaderu 2.7) |
[Poster Presentation]
Compensated Integrated Gradients for Visualization of Features Contributing to EEG Classification Kazuki Tachikawa, Yuji Kawai, Jihoon Park, Minoru Asada (Osaka Univ.) IBISML2018-76 |
Integrated gradients method has been widely employed to evaluate the degrees of contribution of input features to classi... [more] |
IBISML2018-76 pp.241-247 |
SIP, CAS, MSS, VLD |
2017-06-19 13:00 |
Niigata |
Niigata University, Ikarashi Campus |
[Invited Talk]
Composite Variables and Ensemble: Introduction to Forest Regression and Additive Models Ichigaku Takigawa (Hokkaido Univ.) CAS2017-8 VLD2017-11 SIP2017-32 MSS2017-8 |
Machine learning, supervised machine learning in particular, now becomes one of daily tools in signal processing such as... [more] |
CAS2017-8 VLD2017-11 SIP2017-32 MSS2017-8 p.43 |