2022/05/14 更新

イシバシ ヒデアキ
石橋 英朗
ISHIBASHI Hideaki
Scopus 論文情報  
総論文数: 0  総Citation: 0  h-index: 2

Citation Countは当該年に発表した論文の被引用数

所属
大学院生命体工学研究科 人間知能システム工学専攻
職名
助教
外部リンク

取得学位

  • 九州工業大学  -  博士(情報工学)   2018年03月

学内職務経歴

  • 2019年04月 - 現在   九州工業大学   大学院生命体工学研究科   人間知能システム工学専攻     助教

論文

  • Principal Component Analysis for Gaussian Process Posteriors 査読有り 国際誌

    Ishibashi H., Akaho S.

    Neural Computation   34 ( 5 )   1189 - 1219   2022年04月

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    担当区分:筆頭著者, 責任著者   記述言語:英語   掲載種別:研究論文(学術雑誌)

    This letter proposes an extension of principal component analysis for gaussian process (GP) posteriors, denoted by GP-PCA. Since GP-PCA estimates a low-dimensional space of GP posteriors, it can be used for metalearning, a framework for improving the performance of target tasks by estimating a structure of a set of tasks. The issue is how to define a structure of a set of GPs with an infinite-dimensional parameter, such as coordinate system and a divergence. In this study, we reduce the in-finiteness of GP to the finite-dimensional case under the information geometrical framework by considering a space of GP posteriors that have the same prior. In addition, we propose an approximation method of GP-PCA based on variational inference and demonstrate the effectiveness of GP-PCA as meta-learning through experiments.

    DOI: 10.1162/neco_a_01489

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85129060990&origin=inward

  • Multi-task manifold learning for small sample size datasets 査読有り

    Ishibashi H., Higa K., Furukawa T.

    Neurocomputing   473   138 - 157   2022年02月

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    担当区分:筆頭著者   記述言語:英語   掲載種別:研究論文(学術雑誌)

    In this study, we develop a method for multi-task manifold learning. The method aims to improve the performance of manifold learning for multiple tasks, particularly when each task has a small number of samples. Furthermore, the method also aims to generate new samples for new tasks, in addition to new samples for existing tasks. In the proposed method, we use two different types of information transfer: instance transfer and model transfer. For instance transfer, datasets are merged among similar tasks, whereas for model transfer, the manifold models are averaged among similar tasks. For this purpose, the proposed method consists of a set of generative manifold models corresponding to the tasks, which are integrated into a general model of a fiber bundle. We applied the proposed method to artificial datasets and face image sets, and the results showed that the method was able to estimate the manifolds, even for a tiny number of samples.

    DOI: 10.1016/j.neucom.2021.11.043

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85121311100&origin=inward

  • Automated stopping criterion for spectral measurements with active learning 査読有り

    Ueno T., Ishibashi H., Hino H., Ono K.

    npj Computational Materials   7 ( 1 )   2021年12月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)

    The automated stopping of a spectral measurement with active learning is proposed. The optimal stopping of the measurement is realised with a stopping criterion based on the upper bound of the posterior average of the generalisation error of the Gaussian process regression. It is revealed that the automated stopping criterion of the spectral measurement gives an approximated X-ray absorption spectrum with sufficient accuracy and reduced data size. The proposed method is not only a proof-of-concept of the optimal stopping problem in active learning but also the key to enhancing the efficiency of spectral measurements for high-throughput experiments in the era of materials informatics.

    DOI: 10.1038/s41524-021-00606-5

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85113396806&origin=inward

  • Visual analytics of set data for knowledge discovery and member selection support 査読有り

    Watanabe R., Ishibashi H., Furukawa T.

    Decision Support Systems   152   2021年01月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)

    Visual analytics (VA) is a visually assisted exploratory analysis approach in which knowledge discovery is executed interactively between the user and system in a human-centered manner. The purpose of this study is to develop a method for the VA of set data aimed at supporting knowledge discovery and member selection. A typical target application is a visual support system for team analysis and member selection, by which users can analyze past teams and examine candidate lineups for new teams. Because there are several difficulties, such as the combinatorial explosion problem, developing a VA system of set data is challenging. In this study, we first define the requirements that the target system should satisfy and clarify the accompanying challenges. Then we propose a method for the VA of set data, which satisfies the requirements. The key idea is to model the generation process of sets and their outputs using a manifold network model. The proposed method visualizes the relevant factors as a set of topographic maps on which various information is visualized. Furthermore, using the topographic maps as a bidirectional interface, users can indicate their targets of interest in the system on these maps. We demonstrate the proposed method by applying it to basketball teams, and compare with a benchmark system for outcome prediction and lineup reconstruction tasks. Because the method can be adapted to individual application cases by extending the network structure, it can be a general method by which practical systems can be built.

    DOI: 10.1016/j.dss.2021.113635

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85111295128&origin=inward

  • Stopping criterion for active learning based on deterministic generalization bounds 査読有り 国際誌

    Hideaki Ishibashi, Hideitsu Hino

    Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics ( Proceedings of Machine Learning Research )   108   386 - 397   2020年08月

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    担当区分:筆頭著者   記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

    Active learning is a framework in which the learning machine can select the samples to be used for training. This technique is promising, particularly when the cost of data acquisition and labeling is high. In active learning, determining the timing at which learning should be stopped is a critical issue. In this study, we propose a criterion for automatically stopping active learning. The proposed stopping criterion is based on the difference in the expected generalization errors and hypothesis testing. We derive a novel upper bound for the difference in expected generalization errors before and after obtaining a new training datum based on PAC-Bayesian theory. Unlike ordinary PAC-Bayesian bounds, though, the proposed bound is deterministic; hence, there is no uncontrollable trade-off between the confidence and tightness of the inequality. We combine the upper bound with a statistical test to derive a stopping criterion for active learning. We demonstrate the effectiveness of the proposed method via experiments with both artificial and real datasets.

  • Multi-task learning for Self-Organizing Maps 査読有り

    Kazushi Higa, Hideaki Ishibashi, Tetsuo Furukawa

    Proceedings of Joint 10th International Conference on Soft Computing and Intelligent Systems and19th International Symposium on Advanced Intelligent Systems in conjunction with Intelligent Systems Workshop 2018   2018年12月

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    記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

  • Non-parametriccontinuous Self-Organizing Map 査読有り

    Ryuji Watanabe, Hideaki Ishibashi, Tohru Iwasaki, Tetsuo Furukawa

    Proceedings of Joint 10th International Conference on Soft Computing and Intelligent Systems and19th International Symposium on Advanced Intelligent Systemsin conjunction with Intelligent Systems Workshop 2018   2018年12月

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    記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

  • Multi-task manifold learning using hierarchical modeling for insufficient samples 査読有り

    Hideaki Ishibashi, Kazushi Higa, Tetsuo Furukawa

    Lecture Notes in Computer Science   11303   388 - 398   2018年11月

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    記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

    DOI: 10.1007/978-3-030-04182-3_34

  • Visualization method of viewpoints latent in a dataset 査読有り

    Hideaki Ishibashi

    Lecture Notes in Computer Science   11303   638 - 647   2018年11月

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    担当区分:責任著者   記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

    DOI: 10.1007/978-3-030-04182-3_56

  • Hierarchical tensor SOM network for multilevel multigroup analysis 査読有り

    Hideaki Ishibashi, Tetsuo Furukawa

    Proceedings of Neural Processing Letters   47 ( 3 )   1011 - 1025   2018年06月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)

    DOI: 10.1007/s11063-017-9643-1

  • Hierarchical Tensor Manifold Modeling for Multi-Group Analysis 査読有り

    ISHIBASHI Hideaki, ERA Masayoshi, FURUKAWA Tetsuo

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences   101 ( 11 )   1745 - 1755   2018年01月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)

    <p>The aim of this work is to develop a method for the simultaneous analysis of multiple groups and their members based on hierarchical tensor manifold modeling. The method is particularly designed to analyze multiple teams, such as sports teams and business teams. The proposed method represents members' data using a nonlinear manifold for each team, and then these manifolds are further modeled using another nonlinear manifold in the model space. For this purpose, the method estimates the role of each member in the team, and discovers correspondences between members that play similar roles in different teams. The proposed method was applied to basketball league data, and it demonstrated the ability of knowledge discovery from players' statistics. We also demonstrated that the method could be used as a general tool for multi-level multi-group analysis by applying it to marketing data.</p>

    DOI: 10.1587/transfun.E101.A.1745

    CiNii Article

    その他リンク: https://ci.nii.ac.jp/naid/130007502924

  • Multilevel-Multigroup Analysis Discovering Member Correspondence between Groups 査読有り

    Hideaki Ishibashi, Masayoshi Era, Ryota Shinriki, Hirohisa Isogai, Tetsuo Furukawa

    Proceedings of 2017 International Workshop on Smart Info-Media Systems in Asia   2017年09月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)

  • Self-Organizing Maps for Multi-system and Multi-view Datasets(共著) 査読有り

    Ishibashi H., Furukawa T.

    Proceedings - 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016   343 - 348   2016年12月

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    担当区分:筆頭著者   記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

    © 2016 IEEE. In this paper, we describe two types of selforganizing maps (SOMs): a self-organizing system map (SOSM) and a self-organizing view map (SOVM). The task of SOSM is to discover meta-knowledge by estimating the latent system parameter from a series of experiments. The SOSM algorithm is almost identical to the 'SOM of SOMs' (SOM2), with a small modification. SOVM aims to discover meta-knowledge by estimating the latent view parameter. SOSM enables higher-order data structures to be estimated, whereas SOVM enables the consideration of heterogeneous datasets. Simulation results show that these algorithms work as expected.

    DOI: 10.1109/SCIS-ISIS.2016.0078

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85010366338&origin=inward

  • Multilevel-Multigroup analysis using a hierarchical tensor SOM network(共著) 査読有り

    Hideaki Ishibashi, Ryota Shinriki, Hirohisa Isogai, Tetsuo Furukawa

    Lecture Notes in Computer Science   9949   459 - 466   2016年10月

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    記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

    DOI: 10.1007/978-3-319-46675-0_50

    Kyutacar

  • Self-Organizing maps for multi-system and multi-view datasets(共著) 査読有り

    Hideaki Ishibashi, Tetsuo Furukawa

    Proceedings of Joint 8th International Conference on Soft Computing and Intelligent Systems and 17th International Symposium on AdvancedIntelligent Systems2016   343 - 348   2016年08月

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    記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

  • Rating-scale questionnaire survey analysis using SOM-based nonlinear tensor decomposition(共著) 査読有り

    Hideaki Ishibashi, Tohru Iwasaki, Yohsuke Date, Tetsuo Furukawa

    Proceedings of Joint 8th International Conference on Soft Computing and Intelligent Systems and 17th International Symposium on Advanced Intelligent Systems2016   912 - 915   2016年08月

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    記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

▼全件表示

講演

  • 能動学習の停止基準

    第42回IBISML研究会オーガナイズドセッション  2021年03月 

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    発表言語:日本語   講演種別:招待講演  

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