49 citations · 93 across the 7 of their papers we have counts for
Showing stat.MLShow all
2 papers · 1 filter
stat.ML2020★ 30 cited
When is invariance useful in an Out-of-Distribution Generalization problem ?
Masanori Koyama, Shoichiro Yamaguchi
The goal of Out-of-Distribution (OOD) generalization problem is to train a predictor that generalizes on all environments. Popular approaches in this field use the hypothesis that…
stat.ML2019★ 49 cited
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning
Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita +1
Hyperbolic space is a geometry that is known to be well-suited for representation learning of data with an underlying hierarchical structure. In this paper, we present a novel hype…