Effective Connectivity Maps in the Swine Somatosensory Cortex Estimated from Electrocorticography and Validated with Intracortical Local Field Potential Measurements. - Masato Tanosaki; Hideaki Ishibashi; Tongsheng Zhang; Yoshio Okada (Brain Connect., 2014)

Multilevel-Multigroup Analysis Using a Hierarchical Tensor SOM Network. - Hideaki Ishibashi; Ryota Shinriki; Hirohisa Isogai; Tetsuo Furukawa (ICONIP (3), 2016)

Self-Organizing Maps for Multi-system and Multi-view Datasets. - Hideaki Ishibashi; Tetsuo Furukawa (SCIS&ISIS, 2016)

Visualization Method of Viewpoints Latent in a Dataset. - Hideaki Ishibashi (ICONIP (3), 2018)

Multi-task Manifold Learning Using Hierarchical Modeling for Insufficient Samples. - Hideaki Ishibashi; Kazushi Higa; Tetsuo Furukawa (ICONIP (3), 2018)

Hierarchical Tensor Manifold Modeling for Multi-Group Analysis. - Hideaki Ishibashi; Masayoshi Era; Tetsuo Furukawa (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2018)

Hierarchical Tensor SOM Network for Multilevel-Multigroup Analysis. - Hideaki Ishibashi; Tetsuo Furukawa (Neural Process. Lett., 2018)

Stopping criterion for active learning based on deterministic generalization bounds. - Hideaki Ishibashi; Hideitsu Hino (AISTATS, 2020)

Stopping Criterion for Active Learning Based on Error Stability. - Hideaki Ishibashi; Hideitsu Hino (CoRR, 2021)

Low-rank kernel decomposition for scalable manifold modeling. - Kazuki Miyazaki; Shuhei Takano; Ryo Tsuno; Hideaki Ishibashi; Tetsuo Furukawa (SCIS/ISIS, 2022)

Visual analytics of set data for knowledge discovery and member selection support. - Ryuji Watanabe; Hideaki Ishibashi; Tetsuo Furukawa (Decis. Support Syst., 2022)

Multi-task manifold learning for small sample size datasets. - Hideaki Ishibashi; Kazushi Higa; Tetsuo Furukawa (Neurocomputing, 2022)

Principal Component Analysis for Gaussian Process Posteriors. - Hideaki Ishibashi; Shotaro Akaho (Neural Comput., 2022)

A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets. - Hideaki Ishibashi; Masayuki Karasuyama; Ichiro Takeuchi; Hideitsu Hino (AISTATS, 2023)

ATNAS: Automatic Termination for Neural Architecture Search. - Kotaro Sakamoto; Hideaki Ishibashi; Rei Sato; Shinichi Shirakawa; Youhei Akimoto; Hideitsu Hino (Neural Networks, 2023)

An (ε,δ)-accurate level set estimation with a stopping criterion. - Hideaki Ishibashi; Kota Matsui; Kentaro Kutsukake; Hideitsu Hino (CoRR, 2025)