no code implementations • 2 Oct 2020 • katsuhiko Ishiguro, Kazuya Ujihara, Ryohto Sawada, Hirotaka Akita, Masaaki Kotera
Especially, the pre-training plus fine-tuning approach boosts the accuracy scores of the baseline, achieving the new state-of-the-art.
no code implementations • 30 Sep 2019 • Shion Honda, Hirotaka Akita, katsuhiko Ishiguro, Toshiki Nakanishi, Kenta Oono
Statistical generative models for molecular graphs attract attention from many researchers from the fields of bio- and chemo-informatics.
no code implementations • 4 Oct 2018 • Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima
Graphs are general and powerful data representations which can model complex real-world phenomena, ranging from chemical compounds to social networks; however, effective feature extraction from graphs is not a trivial task, and much work has been done in the field of machine learning and data mining.
1 code implementation • 4 Jul 2018 • Hirotaka Akita, Kosuke Nakago, Tomoki Komatsu, Yohei Sugawara, Shin-ichi Maeda, Yukino Baba, Hisashi Kashima
A possible approach to answer this question is to visualize evidence substructures responsible for the predictions.