Masashi Tsubaki

According to our database, Masashi Tsubaki authored at least 10 papers between 2013 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2022

Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers.

Takayuki Hoshino, Suguru Kanoga, Masashi Tsubaki, Atsushi Aoyama

Neurocomputing, 2022

2020

Dual graph convolutional neural network for predicting chemical networks.

Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima

BMC Bioinform., April, 2020

Quantum deep field: data-driven wave function, electron density generation, and atomization energy prediction and extrapolation with machine learning.

Masashi Tsubaki, Teruyasu Mizoguchi

CoRR, 2020

On the equivalence of molecular graph convolution and molecular wave function with poor basis set.

Masashi Tsubaki, Teruyasu Mizoguchi

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Analysis and Usage: Subject-to-subject Linear Domain Adaptation in sEMG Classification.

Takayuki Hoshino, Suguru Kanoga, Masashi Tsubaki, Atsushi Aoyama

Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020

2019

Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences.

Masashi Tsubaki, Kentaro Tomii, Jun Sese

Bioinform., 2019

2018

Dual Convolutional Neural Network for Graph of Graphs Link Prediction.

Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima

CoRR, 2018

Mean-field theory of graph neural networks in graph partitioning.

Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2016

Non-Linear Similarity Learning for Compositionality.

Masashi Tsubaki, Kevin Duh, Masashi Shimbo, Yuji Matsumoto

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2013

Modeling and Learning Semantic Co-Compositionality through Prototype Projections and Neural Networks.

Masashi Tsubaki, Kevin Duh, Masashi Shimbo, Yuji Matsumoto

Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 2013