2023/12/26 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: 4

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

     詳細を見る

    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

    CiNii Research

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

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

    Ishibashi H., Hino H.

    Proceedings of Machine Learning Research   108   386 - 397   2020年01月

     詳細を見る

    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(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.

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85135149649&origin=inward

  • ATNAS: Automatic Termination for Neural Architecture Search 査読有り 国際誌

    Sakamoto K., Ishibashi H., Sato R., Shirakawa S., Akimoto Y., Hino H.

    Neural Networks   166   446 - 458   2023年09月

     詳細を見る

    記述言語:英語   掲載種別:研究論文(学術雑誌)

    Neural architecture search (NAS) is a framework for automating the design process of a neural network structure. While the recent one-shot approaches have reduced the search cost, there still exists an inherent trade-off between cost and performance. It is important to appropriately stop the search and further reduce the high cost of NAS. Meanwhile, the differentiable architecture search (DARTS), a typical one-shot approach, is known to suffer from overfitting. Heuristic early-stopping strategies have been proposed to overcome such performance degradation. In this paper, we propose a more versatile and principled early-stopping criterion on the basis of the evaluation of a gap between expectation values of generalisation errors of the previous and current search steps with respect to the architecture parameters. The stopping threshold is automatically determined at each search epoch without cost. In numerical experiments, we demonstrate the effectiveness of the proposed method. We stop the one-shot NAS algorithms and evaluate the acquired architectures on the benchmark datasets: NAS-Bench-201 and NATS-Bench. Our algorithm is shown to reduce the cost of the search process while maintaining a high performance.

    DOI: 10.1016/j.neunet.2023.07.011

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85167427328&origin=inward

  • A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets 査読有り 国際誌

    Ishibashi H., Karasuyama M., Takeuchi I., Hino H.

    Proceedings of Machine Learning Research   206   6463 - 6497   2023年01月

     詳細を見る

    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(国際会議プロシーディングス)

    Bayesian optimization (BO) improves the efficiency of black-box optimization; however, the associated computational cost and power consumption remain dominant in the application of machine learning methods. This paper proposes a method of determining the stopping time in BO. The proposed criterion is based on the difference between the expectation of the minimum of a variant of the simple regrets before and after evaluating the objective function with a new parameter setting. Unlike existing stopping criteria, the proposed criterion is guaranteed to converge to the theoretically optimal stopping criterion for any choices of arbitrary acquisition functions and threshold values. Moreover, the threshold for the stopping criterion can be determined automatically and adaptively. We experimentally demonstrate that the proposed stopping criterion finds reasonable timing to stop a BO with a small number of evaluations of the objective function.

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85165187394&origin=inward

  • End-condition for solution small angle X-ray scattering measurements by kernel density estimation 査読有り 国際誌

    Hiroshi Sekiguchi, Noboru Ohta, Hideaki Ishibashi, Hideitsu Hino, Masaichiro Mizumaki

    Science and Technology of Advanced Materials: Methods   2022年12月

     詳細を見る

    記述言語:英語   掲載種別:研究論文(学術雑誌)

    DOI: 10.1080/27660400.2022.2140021

    DOI: 10.1080/27660400.2022.2140021

  • 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

     詳細を見る

    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

  • 能動学習によるスペクトル測定の自動停止 査読有り

    上野 哲朗, 石橋 英朗, 日野 英逸, 小野 寛太

    人工知能学会全国大会論文集 ( 一般社団法人 人工知能学会 )   JSAI2022 ( 0 )   3Yin208 - 3Yin208   2022年01月

     詳細を見る

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

    <p>近年のマテリアルズ・インフォマティクスの興隆にともなって、物質科学における様々な実験の効率化、自動化・自律化が望まれている。我々は物質科学の主要な実験手法のひとつであるスペクトル測定を、能動学習を用いて効率化する手法を開発した。獲得関数が最大となるエネルギー点を順次計測していく能動学習によって、実験者が介在することなく自動かつ最適な条件でのスペクトル測定を実現した。ガウス過程回帰の期待汎化誤差の上界に基づく停止基準を用いることで、スペクトルの種類に依らず測定の自動停止が可能になった。これによって従来のスペクトル計測に対して少数の計測点で同等の物質情報を得ることができる。</p>

    DOI: 10.11517/pjsai.jsai2022.0_3yin208

    CiNii Research

  • ニューラルアーキテクチャサーチの最適停止 査読有り

    坂本 航太郎, 石橋 英朗, 佐藤 怜, 白川 真一, 秋本 洋平, 日野 英逸

    人工知能学会全国大会論文集 ( 一般社団法人 人工知能学会 )   JSAI2022 ( 0 )   3J4OS3b01 - 3J4OS3b01   2022年01月

     詳細を見る

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

    <p>【背景】深層学習によって特徴量設計からニューラルネットの構造設計にパラダイムシフトが起こったが, タスクに応じた構造設計は難しい. そこでニューラルネットの構造の自動探索手法―ニューラルアーキテクチャサーチ (<i>Neural architecture search</i>: NAS)―が盛んに研究されている. 最新手法では低コストでの構造探索に成功している. しかし, 1) 精度と探索コストにはトレードオフがある. 2) 性能劣化を防ぐための早期停止が有効である. ということが報告されており, いつ探索を停止するか?という課題が存在する. 【目的】NASの最適停止手法を提案する. 性能の良い構造を効率よく探索することを目的とする. 【結果】数値実験により提案手法が有効であることを検証した.</p>

    DOI: 10.11517/pjsai.jsai2022.0_3j4os3b01

    CiNii Research

  • Low-rank kernel decomposition for scalable manifold modeling 査読有り 国際誌

    Miyazaki K., Takano S., Tsuno R., Ishibashi H., Furukawa T.

    2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2022   2022年01月

     詳細を見る

    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)

    The purpose of this study is to develop a method for scalable manifold modeling. A popular method using Gaussian process requires a computational cost of the cubic order for data size, which does not afford to apply large-scale datasets. We aim to achieve the linear order in computational cost using unsupervised kernel regression and its sparse approximation instead of using the Gaussian process regression. For this purpose, we introduce two types of sparse approximations; one is the discretization of the latent space by a grid of inducing points, and the other is a sparse matrix decomposition of the local and global kernel matrices. We evaluated the computation time of the proposed method by using an artificial dataset, and the results showed that the proposed method achieved the linear order in computation time.

    DOI: 10.1109/SCISISIS55246.2022.10001865

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85146734984&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

     詳細を見る

    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

     詳細を見る

    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

  • 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

     詳細を見る

    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

     詳細を見る

    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 国際誌

    Hideaki Ishibashi, Kazushi Higa, Tetsuo Furukawa

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

     詳細を見る

    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)

    In this paper, we propose a method for multi-task manifold learning. For a set of tasks of dimensionality reduction, the aim of the method is to model each given dataset as a manifold, and map it to a low-dimensional space. For this purpose, we use a hierarchical manifold modeling approach. Thus, while each data distribution is represented by a manifold model, the obtained models are further modeled by a higher-order manifold in a function space. The higher-order model mediates the information transfer between tasks, and as a result, the performance of each task is improved. The results of simulations show that the proposed method can estimate manifolds approximately, even in cases in which a tiny number of samples are provided for each task.

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

    Kyutacar

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85059020589&origin=inward

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

    Hideaki Ishibashi

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

     詳細を見る

    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(国際会議プロシーディングス)

    The purpose of this study is to propose a paradigm visualizing the viewpoints from datasets observed by multiple viewpoints, which is referred to as Latent Viewpoint Visualization (LVV). Since LVV visualizes similarity/dissimilarity among the viewpoints, it has many applications such as the authors’ perspective from news articles and the psychological measurements’ aspect from psychological surveys. In this study, we propose the concept of LVV and develop a preliminary algorithm. Furthermore, we experimentally show what kind of information can be visualized by LVV using several datasets.

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

    Kyutacar

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85059046342&origin=inward

  • 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

     詳細を見る

    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)

    The aim of this work is to develop a visualization method for multilevel–multigroup analysis based on a multiway nonlinear dimensionality reduction. The task of the method is to visualize what kinds of members each group is composed and to visualize the similarity between the groups in terms of probability distribution of constituent members. To achieve the task, the proposed method consists of hierarchically coupled tensor self-organizing maps, corresponding to the member/group level. This architecture enables more flexible analysis than ordinary parametric multilevel analysis, as it retains a high level of interpretability supported by strong visualization. We applied the proposed method to one benchmark dataset and two practical datasets: one is the survey data on the football players belonging to different teams and the other is the employee survey data belonging to different departments in a company. Our method successfully visualizes the types of the members that constitute each group as well as visualizes the differences or similarities between the groups.

    DOI: 10.1007/s11063-017-9643-1

    Kyutacar

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85020251157&origin=inward

  • 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

     詳細を見る

    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

     詳細を見る

    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)

    Kyutacar

  • Self-Organizing Maps for Multi-system and Multi-view Datasets 査読有り 国際誌

    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

     詳細を見る

    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 査読有り 国際誌

    Hideaki Ishibashi, Ryota Shinriki, Hirohisa Isogai, Tetsuo Furukawa

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

     詳細を見る

    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)

    This paper describes a method of multilevel–multigroup analysis based on a nonlinear multiway dimensionality reduction. To analyze a set of groups in terms of the probabilistic distribution of their constituent member data, the proposed method uses a hierarchical pair of tensor self-organizing maps (TSOMs), one for the member analysis and the other for the group analysis. This architecture enables more flexible analysis than ordinary parametric multilevel analysis, as it retains a high level of translatability supported by strong visualization. Furthermore, this architecture provides a consistent and seamless computation method for multilevel–multigroup analysis by integrating two different levels into a hierarchical tensor SOM network. The proposed method is applied to a dataset of football teams in a university league, and successfully visualizes the types of players that constitute each team as well as the differences or similarities between the teams.

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

    Kyutacar

    Scopus

    その他リンク: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84992745797&origin=inward

  • Rating-scale questionnaire survey analysis using SOM-based nonlinear tensor decomposition 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

     詳細を見る

    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)

    Kyutacar

▼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 

     詳細を見る

    発表言語:日本語   講演種別:招待講演