Modeling and Learning Semantic Co-Compositionality through Prototype Projections and Neural Networks. - Masashi Tsubaki; Kevin Duh; Masashi Shimbo; Yuji Matsumoto 001 (EMNLP, 2013)

Non-Linear Similarity Learning for Compositionality. - Masashi Tsubaki; Kevin Duh; Masashi Shimbo; Yuji Matsumoto 001 (AAAI, 2016)

Mean-field theory of graph neural networks in graph partitioning. - Tatsuro Kawamoto; Masashi Tsubaki; Tomoyuki Obuchi (NeurIPS, 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)

Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. - Masashi Tsubaki; Kentaro Tomii; Jun Sese (Bioinform., 2019)

Analysis and Usage: Subject-to-subject Linear Domain Adaptation in sEMG Classification. - Takayuki Hoshino; Suguru Kanoga; Masashi Tsubaki; Atsushi Aoyama (EMBC, 2020)

On the equivalence of molecular graph convolution and molecular wave function with poor basis set. - Masashi Tsubaki; Teruyasu Mizoguchi (NeurIPS, 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)

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., 2020)

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)

Comparison of fine-tuned single-source and multi-source approaches to surface electromyogram pattern recognition. - Takayuki Hoshino; Suguru Kanoga; Masashi Tsubaki; Atsushi Aoyama (Biomed. Signal Process. Control., 2024)