椿真史 (つばきまさし)
sy国立研究開発法人 産業技術総合研究所 人工知能研究センター 機械学習研究チーム 主任研究員
物理学、化学、生物学などの自然科学データに対する機械学習について、基礎的な手法開発から材料や医薬品の応用研究まで幅広く行っています。最近は主に、深層学習、量子化学、密度汎関数理論の融合領域の研究を行っています。
Email: x@aist.go.jp, where x = tsubaki.masashi
Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers [paper]
Takayuki Hoshino, Suguru Kanoga, Masashi Tsubaki, and Atsushi Aoyama
Neurocomputing, 2021
Quantum deep descriptor: physically informed transfer learning from small molecules to polymers [paper]
Masashi Tsubaki and Teruyasu Mizoguchi
Journal of Chemical Theory and Computation, 2021
Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation with machine learning [paper] [code]
Masashi Tsubaki and Teruyasu Mizoguchi
Physical Review Letters, 2020
On the equivalence of molecular graph convolution and molecular wave function with poor basis set [paper] [code]
Masashi Tsubaki and Teruyasu Mizoguchi
Advances in Neural Information Processing Systems (NeurIPS 2020)
Analysis and usage: subject-to-subject linear domain adaptation in sEMG classification [paper]
Takayuki Hoshino, Suguru Kanoga, Masashi Tsubaki, and Atsushi Aoyama
42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020)
Learning excited states from ground states by using an artificial neural network [paper]
Shin Kiyohara, Masashi Tsubaki, and Teruyasu Mizoguchi
npj Computational Materials, 2020
Dual graph convolutional neural network for predicting chemical networks [paper]
Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, and Hisashi Kashima
BMC Bioinformatics, 2020
Uncovering prognosis-related genes and pathways by multi-omics analysis in lung cancer [paper]
Ken Asada, Kazuma Kobayashi, Samuel Joutard, Masashi Tsubaki, Satoshi Takahashi, Ken Takasawa, Masaaki Komatsu, Syuzo Kaneko, Jun Sese, and Ryuji Hamamoto
Biomolecules, 2020
Quantitative estimation of properties from core-loss spectrum via neural network [paper]
Shin Kiyohara, Masashi Tsubaki, Kunyen Liao, and Teruyasu Mizoguchi
Journal of Physics: Materials, 2019
Mean-field theory of graph neural networks in graph partitioning [paper]
Tatsuro Kawamoto, Masashi Tsubaki, and Tomoyuki Obuchi
Advances in Neural Information Processing Systems (NIPS 2018)
Fast and accurate molecular property prediction: learning atomic interactions and potentials with neural networks [paper] [code] [errata]
Masashi Tsubaki and Teruyasu Mizoguchi
The Journal of Physical Chemistry Letters, 2018
Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences [paper] [code1] [code2]
Masashi Tsubaki, Kentaro Tomii, and Jun Sese
Bioinformatics, 2018