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ORCIDHideaki Ishibashi
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2020 – today
- 2023
[j7]Kotaro Sakamoto
, Hideaki Ishibashi, Rei Sato, Shinichi Shirakawa, Youhei Akimoto, Hideitsu Hino:
ATNAS: Automatic Termination for Neural Architecture Search. Neural Networks 166: 446-458 (2023)- [c7]Hideaki Ishibashi, Masayuki Karasuyama, Ichiro Takeuchi, Hideitsu Hino:
A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets. AISTATS 2023: 6463-6497 - 2022
- [j6]Ryuji Watanabe, Hideaki Ishibashi, Tetsuo Furukawa:
Visual analytics of set data for knowledge discovery and member selection support. Decis. Support Syst. 152: 113635 (2022) - [j5]Hideaki Ishibashi, Kazushi Higa, Tetsuo Furukawa:
Multi-task manifold learning for small sample size datasets. Neurocomputing 473: 138-157 (2022) - [j4]Hideaki Ishibashi, Shotaro Akaho:
Principal Component Analysis for Gaussian Process Posteriors. Neural Comput. 34(5): 1189-1219 (2022) - [c6]Kazuki Miyazaki, Shuhei Takano, Ryo Tsuno, Hideaki Ishibashi, Tetsuo Furukawa:
Low-rank kernel decomposition for scalable manifold modeling. SCIS/ISIS 2022: 1-6 - 2021
- [i5]Hideaki Ishibashi, Hideitsu Hino:
Stopping Criterion for Active Learning Based on Error Stability. CoRR abs/2104.01836 (2021) - [i4]Ryuji Watanabe, Hideaki Ishibashi, Tetsuo Furukawa:
Visual analytics of set data for knowledge discovery and member selection support. CoRR abs/2104.09231 (2021) - [i3]Hideaki Ishibashi, Shotaro Akaho:
Principal component analysis for Gaussian process posteriors. CoRR abs/2107.07115 (2021) - [i2]Hideaki Ishibashi, Kazushi Higa, Tetsuo Furukawa:
Multi-task manifold learning for small sample size datasets. CoRR abs/2111.11655 (2021) - 2020
- [c5]Hideaki Ishibashi, Hideitsu Hino:
Stopping criterion for active learning based on deterministic generalization bounds. AISTATS 2020: 386-397 - [i1]Hideaki Ishibashi, Hideitsu Hino:
Stopping criterion for active learning based on deterministic generalization bounds. CoRR abs/2005.07402 (2020)
2010 – 2019
- 2018
- [j3]Hideaki Ishibashi, Masayoshi Era, Tetsuo Furukawa:
Hierarchical Tensor Manifold Modeling for Multi-Group Analysis. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 101-A(11): 1745-1755 (2018) - [j2]Hideaki Ishibashi, Tetsuo Furukawa:
Hierarchical Tensor SOM Network for Multilevel-Multigroup Analysis. Neural Process. Lett. 47(3): 1011-1025 (2018) - [c4]Hideaki Ishibashi, Kazushi Higa, Tetsuo Furukawa:
Multi-task Manifold Learning Using Hierarchical Modeling for Insufficient Samples. ICONIP (3) 2018: 388-398 - [c3]Hideaki Ishibashi:
Visualization Method of Viewpoints Latent in a Dataset. ICONIP (3) 2018: 638-647 - 2016
- [c2]Hideaki Ishibashi, Ryota Shinriki, Hirohisa Isogai, Tetsuo Furukawa:
Multilevel-Multigroup Analysis Using a Hierarchical Tensor SOM Network. ICONIP (3) 2016: 459-466 - [c1]Hideaki Ishibashi, Tetsuo Furukawa:
Self-Organizing Maps for Multi-system and Multi-view Datasets. SCIS&ISIS 2016: 343-348 - 2014
- [j1]Masato Tanosaki, Hideaki Ishibashi, Tongsheng Zhang, Yoshio Okada:
Effective Connectivity Maps in the Swine Somatosensory Cortex Estimated from Electrocorticography and Validated with Intracortical Local Field Potential Measurements. Brain Connect. 4(2): 100-111 (2014)
Coauthor Index
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