107 citations · 120 across the 3 of their papers we have counts for
3 papers
cs.LG2018★ 107 cited
Deep Learning Model for Finding New Superconductors
Tomohiko Konno, Hodaka Kurokawa, Fuyuki Nabeshima +4
Exploration of new superconductors still relies on the experience and intuition of experts and is largely a process of experimental trial and error. In one study, only 3% of the ca…
cs.LG2018★ 10 cited
Icing on the Cake: An Easy and Quick Post-Learnig Method You Can Try After Deep Learning
Tomohiko Konno, Michiaki Iwazume
We found an easy and quick post-learning method named "Icing on the Cake" to enhance a classification performance in deep learning. The method is that we train only the final class…
cs.LG2018★ 3 cited
Cavity Filling: Pseudo-Feature Generation for Multi-Class Imbalanced Data Problems in Deep Learning
Tomohiko Konno, Michiaki Iwazume
Herein, we generate pseudo-features based on the multivariate probability distributions obtained from the feature maps in layers of trained deep neural networks. Further, we augmen…