activity
20182025
most citedGirth, magnitude homology, and phase transition of diagonality

2 citations · 4 across the 5 of their papers we have counts for

collaborators

10 papers

math.MG2025

Geometric interpretation of magnitude

Yasuhiko Asao, Kiyonori Gomi

For an positive definite symmetric matrix with for all , we show that there exists a set of vectors such that the radius $…

math.AT2024

Magnitude homology and homotopy type of metric fibrations

Yasuhiko Asao, Yu Tajima, Masahiko Yoshinaga

In this article, we show that each two metric fibrations with a common base and a common fiber have isomorphic magnitude homology, and even more, the same magnitude homotopy type.…

math.AT2024

Minimal projective resolution and magnitude homology of geodetic metric spaces

Yasuhiko Asao, Shun Wakatsuki

Asao-Ivanov showed that magnitude homology is a Tor functor, hence we can compute it by giving a projective resolution of a certain module. In this article, we compute magnitude ho…

math.KT2024

Magnitude homology is a derived functor

Yasuhiko Asao, Sergei O. Ivanov

We prove that the magnitude (co)homology of an enriched category can, under some technical assumptions, be described in terms of derived functors between certain abelian categories…

math.AT2023

Classification of metric fibrations

Yasuhiko Asao

In this paper, we study `a fibration of metric spaces' that was originally introduced by Leinster in the study of the magnitude and called metric fibrations. He showed that the mag…

stat.ML20222 cited

Convergence of neural networks to Gaussian mixture distribution

Yasuhiko Asao, Ryotaro Sakamoto, Shiro Takagi

We give a proof that, under relatively mild conditions, fully-connected feed-forward deep random neural networks converge to a Gaussian mixture distribution as only the width of th…