Kenta Oono

Preferred Networks (Japan) — Japan

Computer Science · Physical Sciences

10h-index537citations28works7.02yr avg
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Research Topics

Neural Networks and Applications(8), Model Reduction and Neural Networks(6), Advanced Graph Neural Networks(5), RNA modifications and cancer(3), Machine Learning in Materials Science(3)

Publications28 total

Creating a general-purpose generative model for healthcare data based on multiple clinical studies
PLOS Digital Health·2025Open Access
Creating a General-Purpose Generative Model for Healthcare Data based on Multiple Clinical Studies
2025· 1 citedOpen Access
GenerRNA: A generative pre-trained language model for de novo RNA design
PLoS ONE·2024· 23 citedOpen Access
GenerRNA: A generative pre-trained language model for <i>de novo</i> RNA design
2024· 4 citedOpen Access
Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics
arXiv (Cornell University)·2023Open Access
Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network
arXiv (Cornell University)·2023Open Access
Assessment of Multidimensional Health Care Parameters Among Adults in Japan for Developing a Virtual Human Generative Model: Protocol for a Cross-sectional Study
JMIR Research Protocols·2023· 2 citedOpen Access
TabRet: Pre-training Transformer-based Tabular Models for Unseen Columns
arXiv (Cornell University)·2023Open Access
Assessment of Multidimensional Health Care Parameters Among Adults in Japan for Developing a Virtual Human Generative Model: Protocol for a Cross-sectional Study (Preprint)
2023Open Access
Prediction of tissue-of-origin of early stage cancers using serum miRNomes
JNCI Cancer Spectrum·2022· 31 citedOpen Access
Universal approximation property of invertible neural networks
arXiv (Cornell University)·2022· 9 citedOpen Access
Fast Estimation Method for the Stability of Ensemble Feature Selectors
arXiv (Cornell University)·2021Open Access
Universal Approximation Property of Neural Ordinary Differential Equations
arXiv (Cornell University)·2020· 20 citedOpen Access
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
arXiv (Cornell University)·2020· 42 citedOpen Access
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
Neural Information Processing Systems·2020· 8 cited
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks
arXiv (Cornell University)·2020· 5 citedOpen Access
Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks
arXiv (Cornell University)·2020· 2 citedOpen Access
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
International Conference on Learning Representations·2020· 112 cited
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks
Neural Information Processing Systems·2020· 6 cited
Graph Residual Flow for Molecular Graph Generation
arXiv (Cornell University)·2019· 31 citedOpen Access
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Frequent Co-authors

Taiji Suzuki(7), Shin‐ichi Maeda(5), Kotatsu Bito(5), Nontawat Charoenphakdee(5), Zhengyan Gao(5), Isao Ishikawa(4), Koichi Tojo(4), Masanobu Hibi(4), Shun Katada(4), Aya Kawakami(4), Hiroshi Maruyama(4), Takeshi Teshima(3), Masahiro Ikeda(3), Masashi Sugiyama(3), Masaaki Kotera(3), Kohei Hayashi(3), Yuki Saito(3), Yoshiaki Ota(3), Shoichiro Yamaguchi(3), Yohei Sugawara(3)