| Change-point detection in time-series data by relative density-ratio estimation S Liu, M Yamada, N Collier, M Sugiyama Neural Networks 43, 72-83, 2013 | 634 | 2013 |
| Intelligent image-activated cell sorting N Nitta, T Sugimura, A Isozaki, H Mikami, K Hiraki, S Sakuma, T Iino, ... Cell 175 (1), 266-276. e13, 2018 | 535 | 2018 |
| Graphlime: Local interpretable model explanations for graph neural networks Q Huang, M Yamada, Y Tian, D Singh, Y Chang IEEE Transactions on Knowledge and Data Engineering 35 (7), 6968-6972, 2022 | 481 | 2022 |
| High-dimensional feature selection by feature-wise kernelized lasso M Yamada, W Jitkrittum, L Sigal, EP Xing, M Sugiyama Neural computation 26 (1), 185-207, 2014 | 397 | 2014 |
| Transformer dissection: a unified understanding of transformer's attention via the lens of kernel YHH Tsai, S Bai, M Yamada, LP Morency, R Salakhutdinov arXiv preprint arXiv:1908.11775, 2019 | 294 | 2019 |
| Relative density-ratio estimation for robust distribution comparison M Yamada, T Suzuki, T Kanamori, H Hachiya, M Sugiyama Neural computation 25 (5), 1324-1370, 2013 | 277 | 2013 |
| Random features strengthen graph neural networks R Sato, M Yamada, H Kashima Proceedings of the 2021 SIAM international conference on data mining (SDM …, 2021 | 262 | 2021 |
| Semantic correspondence as an optimal transport problem Y Liu, L Zhu, M Yamada, Y Yang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 153 | 2020 |
| High-throughput imaging flow cytometry by optofluidic time-stretch microscopy C Lei, H Kobayashi, Y Wu, M Li, A Isozaki, A Yasumoto, H Mikami, T Ito, ... Nature protocols 13 (7), 1603-1631, 2018 | 151 | 2018 |
| Approximation ratios of graph neural networks for combinatorial problems R Sato, M Yamada, H Kashima Advances in Neural Information Processing Systems 32, 2019 | 135 | 2019 |
| Noise suppressing device M Yamada, K Kondo US Patent App. 13/005,138, 2011 | 121 | 2011 |
| Information-theoretic Semi-supervised Metric Learning via Entropy Regularization G Niu, B Dai, M Yamada, M Sugiyama Arxiv preprint arXiv:1206.4614, 2012 | 109 | 2012 |
| Tree-sliced variants of Wasserstein distances T Le, M Yamada, K Fukumizu, M Cuturi Advances in neural information processing systems 32, 2019 | 100 | 2019 |
| Persistence fisher kernel: A riemannian manifold kernel for persistence diagrams T Le, M Yamada Advances in neural information processing systems 31, 2018 | 100 | 2018 |
| Change-point detection with feature selection in high-dimensional time-series data M Yamada, A Kimura, F Naya, H Sawada Twenty-Third International Joint Conference on Artificial Intelligence, 2013 | 96 | 2013 |
| A practical guide to intelligent image-activated cell sorting A Isozaki, H Mikami, K Hiramatsu, S Sakuma, Y Kasai, T Iino, T Yamano, ... Nature protocols 14 (8), 2370-2415, 2019 | 92 | 2019 |
| Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data H Climente-González, CA Azencott, S Kaski, M Yamada Bioinformatics 35 (14), i427-i435, 2019 | 91 | 2019 |
| Ultra high-dimensional nonlinear feature selection for big biological data M Yamada, J Tang, J Lugo-Martinez, E Hodzic, R Shrestha, A Saha, ... IEEE Transactions on Knowledge and Data Engineering 30 (7), 1352-1365, 2018 | 89 | 2018 |
| Beyond ranking: Optimizing whole-page presentation Y Wang, D Yin, L Jie, P Wang, M Yamada, Y Chang, Q Mei Proceedings of the Ninth ACM International Conference on Web Search and Data …, 2016 | 89 | 2016 |
| Clustering-based anomaly detection in multi-view data A Marcos Alvarez, M Yamada, A Kimura, T Iwata Proceedings of the 22nd ACM international conference on Information …, 2013 | 88 | 2013 |
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of Tokyok.u-tokyo.ac.jp의 이메일 확인됨