| Acceleration of X-ray computed tomography scanning with high-quality reconstructed volume by deblurring transmission images using convolutional neural networks R Yuki, Y Ohtake, H Suzuki Precision Engineering 73, 153-165, 2022 | 7 | 2022 |
| Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis K Yamanishi, L Xu, R Yuki, S Fukushima, C Lin Scientific Reports 11 (1), 19795, 2021 | 5 | 2021 |
| Dimensionality selection of hyperbolic graph embeddings using decomposed normalized maximum likelihood code-length R Yuki, Y Ike, K Yamanishi 2022 IEEE International Conference on Data Mining (ICDM), 666-675, 2022 | 4 | 2022 |
| Dimensionality selection for hyperbolic embeddings using decomposed normalized maximum likelihood code-length R Yuki, Y Ike, K Yamanishi Knowledge and Information Systems 65 (12), 5601-5634, 2023 | 3 | 2023 |
| Deblurring Sinograms Using a Covolutional Neural Network to Achieve Fast X-ray Computed Tomography Scanning R Yuki, Y Ohtake, H Suzuki e-Journal of Nondestructive Testing 25 (2), 2020 | 2 | 2020 |
| Deblurring X-ray transmission images using convolutional neural networks to achieve fast CT scanning R Yuki, Y Ohtake, H Suzuki 10th Conference on Industrial Computed Tomography, Wels, Austria (iCT 2020), 2020 | 2 | 2020 |
| Clustering Change Sign Detection by Fusing Mixture Complexity K Urano, R Yuki, K Yamanishi arXiv preprint arXiv:2403.18269, 2024 | | 2024 |
| Dimensionality and Curvature Selection of Graph Embedding using Decomposed Normalized Maximum Likelihood Code-Length R Yuki, A Suzuki, K Yamanishi 2023 IEEE International Conference on Data Mining (ICDM), 1517-1522, 2023 | | 2023 |
| Dimensionality and Curvature Selection of Graph Embedding using DNML Code-Length R Yuki, A Suzuki, K Yamanishi IEEE, 2023 | | 2023 |
| Detecting Change Signs with Differential MDL Change Statistics for COVID-19 Pandemic Analysis K Yamanishi, L Xu, R Yuki, S Fukushima, C Lin arXiv preprint arXiv:2007.15179, 2020 | | 2020 |
| Learning Sparse Representation of Graph Embedding with General Similarities Using Grouplasso and Luckiness Normalized Maximum Likelihood Code-Length R Yuki, S Akiyama, A Suzuki, K Yamanishi Available at SSRN 4663084, 0 | | |
