关注
hideaki ishibashi
hideaki ishibashi
在 brain.kyutech.ac.jp 的电子邮件经过验证
标题
引用次数
引用次数
年份
Stopping criterion for active learning based on deterministic generalization bounds
H Ishibashi, H Hino
International Conference on Artificial Intelligence and Statistics, 386-397, 2020
322020
Automated stopping criterion for spectral measurements with active learning
T Ueno, H Ishibashi, H Hino, K Ono
npj Computational Materials 7 (1), 139, 2021
222021
Multi-task manifold learning for small sample size datasets
H Ishibashi, K Higa, T Furukawa
Neurocomputing 473, 138-157, 2022
152022
Stopping criterion for active learning based on error stability
H Ishibashi, H Hino
arXiv preprint arXiv:2104.01836, 2021
132021
A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets
H Ishibashi, M Karasuyama, I Takeuchi, H Hino
International Conference on Artificial Intelligence and Statistics, 6463-6497, 2023
82023
Visual analytics of set data for knowledge discovery and member selection support
R Watanabe, H Ishibashi, T Furukawa
Decision Support Systems 152, 113635, 2022
72022
Hierarchical tensor SOM network for multilevel–multigroup analysis
H Ishibashi, T Furukawa
Neural Processing Letters 47, 1011-1025, 2018
72018
Principal component analysis for Gaussian process posteriors
H Ishibashi, S Akaho
Neural Computation 34 (5), 1189-1219, 2022
52022
Multilevel–multigroup analysis using a hierarchical tensor SOM network
H Ishibashi, R Shinriki, H Isogai, T Furukawa
Neural Information Processing: 23rd International Conference, ICONIP 2016 …, 2016
52016
ATNAS: Automatic Termination for Neural Architecture Search
K Sakamoto, H Ishibashi, R Sato, S Shirakawa, Y Akimoto, H Hino
Neural Networks 166, 446-458, 2023
12023
Self-Organizing Maps for Multi-system and Multi-view Datasets
H Ishibashi, T Furukawa
2016 Joint 8th International Conference on Soft Computing and Intelligent …, 2016
12016
End-condition for solution small angle X-ray scattering measurements by kernel density estimation
H Sekiguchi, N Ohta, H Ishibashi, H Hino, M Mizumaki
Science and Technology of Advanced Materials: Methods 2 (1), 426-434, 2022
2022
Low-rank kernel decomposition for scalable manifold modeling
K Miyazaki, S Takano, R Tsuno, H Ishibashi, T Furukawa
2022 Joint 12th International Conference on Soft Computing and Intelligent …, 2022
2022
What is the true objective of multi-task manifold modeling?--Comparison of maximum likelihood and optimal transport approaches
R Tsuno, H Ishibashi, T Furukawa
IEICE Technical Report; IEICE Tech. Rep. 120 (403), 53-58, 2021
2021
Visualization tool for basketball team performance by multi-level SOM
K Senoura, H Ishibashi, T Furukawa
IEICE Technical Report; IEICE Tech. Rep. 119 (382), 27-31, 2020
2020
Dimensionality reduction method for gaussian process posteriors based on information geometry
H Ishibashi, S Akaho
IEICE Technical Report; IEICE Tech. Rep. 119 (360), 17-24, 2020
2020
Hierarchical Tensor Manifold Modeling for Multi-Group Analysis
H Ishibashi, M Era, T Furukawa
IEICE Transactions on Fundamentals of Electronics, Communications and …, 2018
2018
Multi-task manifold learning using hierarchical modeling for insufficient samples
H Ishibashi, K Higa, T Furukawa
Neural Information Processing: 25th International Conference, ICONIP 2018 …, 2018
2018
Visualization Method of Viewpoints Latent in a Dataset
H Ishibashi
Neural Information Processing: 25th International Conference, ICONIP 2018 …, 2018
2018
An attempt of continuous latent variable model by non-negative kernel smoother
H Ishibashi, T Iwasaki, R Watanabe, T Furukawa
IEICE Technical Report; IEICE Tech. Rep. 117 (325), 29-34, 2017
2017
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