| Chainer: a next-generation open source framework for deep learning S Tokui, K Oono, S Hido, J Clayton Proceedings of workshop on machine learning systems (LearningSys) in the …, 2015 | 1128 | 2015 |
| Graph Neural Networks Exponentially Lose Expressive Power for Node Classification K Oono, T Suzuki International Conference on Learning Representations (ICLR 2020), 2019 | 390 | 2019 |
| Population-based de novo molecule generation, using grammatical evolution N Yoshikawa, K Terayama, M Sumita, T Homma, K Oono, K Tsuda Chemistry Letters 47 (11), 1431-1434, 2018 | 138 | 2018 |
| Coupling-based invertible neural networks are universal diffeomorphism approximators T Teshima, I Ishikawa, K Tojo, K Oono, M Ikeda, M Sugiyama Advances in Neural Information Processing Systems 33, 3362-3373, 2020 | 127 | 2020 |
| Generative machine learning systems for drug design O Kenta, J Clayton, N Ota US Patent 10,776,712, 2020 | 102 | 2020 |
| Approximation and non-parametric estimation of ResNet-type convolutional neural networks K Oono, T Suzuki Proceedings of the 36th International Conference on Machine Learning 97 …, 2019 | 83 | 2019 |
| Graph residual flow for molecular graph generation S Honda, H Akita, K Ishiguro, T Nakanishi, K Oono arXiv preprint arXiv:1909.13521, 2019 | 59 | 2019 |
| Universal approximation property of neural ordinary differential equations T Teshima, K Tojo, M Ikeda, I Ishikawa, K Oono arXiv preprint arXiv:2012.02414, 2020 | 53 | 2020 |
| Abnormality detection system, abnormality detection method, abnormality detection program, and method for generating learned model D Okanohara, O Kenta US Patent 10,831,577, 2020 | 40 | 2020 |
| Systems and methods for multimodal generative machine learning O Kenta, J Clayton US Patent App. 16/070,276, 2019 | 37 | 2019 |
| Optimization and generalization analysis of transduction through gradient boosting and application to multi-scale graph neural networks K Oono, T Suzuki Advances in Neural Information Processing Systems 33, 18917-18930, 2020 | 35 | 2020 |
| Universal approximation property of invertible neural networks I Ishikawa, T Teshima, K Tojo, K Oono, M Ikeda, M Sugiyama Journal of Machine Learning Research 24 (287), 1-68, 2023 | 34 | 2023 |
| Semi-supervised learning of hierarchical representations of molecules using neural message passing H Nguyen, S Maeda, K Oono arXiv preprint arXiv:1711.10168, 2017 | 21 | 2017 |
| Prediction of tissue-of-origin of early stage cancers using serum miRNomes J Matsuzaki, K Kato, K Oono, N Tsuchiya, K Sudo, A Shimomura, ... JNCI Cancer Spectrum 7 (1), pkac080, 2023 | 18 | 2023 |
| Information processing device and information processing method S Tokui, Y Unno, O Kenta, R Okuta US Patent 11,521,070, 2022 | 15 | 2022 |
| Tabret: Pre-training transformer-based tabular models for unseen columns S Onishi, K Oono, K Hayashi arXiv preprint arXiv:2303.15747, 2023 | 12 | 2023 |
| Abnormality detection system, abnormality detection method, abnormality detection program, and method for generating learned model D Okanohara, O Kenta US Patent 11,334,407, 2022 | 7 | 2022 |
| Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network Y Kinoshita, K Oono, K Fukumizu, Y Yoshida, S Maeda Proceedings of the 40th International Conference on Machine Learning 202 …, 2023 | 6 | 2023 |
| Weisfeiler-Lehman embedding for molecular graph neural networks K Ishiguro, K Oono, K Hayashi arXiv preprint arXiv:2006.06909, 2020 | 6 | 2020 |
| Testing properties of functions on finite groups K Oono, Y Yoshida Random Structures & Algorithms 49 (3), 579-598, 2016 | 5 | 2016 |
Seiya TokuiPreferred Networks, Inc.Verified email at preferred.jp
