I am a researcher with the Machine Learning Research Team of the Artificial Intelligence Research Center (AIRC) at National Institute of Advanced Industrial Science and Technology (AIST), Japan.
I am interested in the physically-informed machine learning applications for biology, chemistry, and physics. In particular, my current focus is the deep learning, quantum chemistry, and density functional theory.
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, K Tomii, J Sese
Bioinformatics, 2019
Protein fold recognition with representation learning and long short-term memory
Masashi Tsubaki, M Shimbo, Y Matsumoto
IPSJ Transactions on Bioinformatics, 2017
Non-linear similarity learning for compositionality
Masashi Tsubaki, K Duh, M Shimbo, Y Matsumoto
Association for the Advancement of Artificial Intelligence (AAAI 2016）
Modeling and learning semantic co-compositionality through prototype projections and neural networks
Masashi Tsubaki, K Duh, M Shimbo, Y Matsumoto
Conference on Empirical Methods in Natural Language Processing (EMNLP 2013)