Skip to content
Log in
Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition

Author

Yoshiki Ueoka

View on Semantic Scholar

4 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#edge computing Open access Sep 2026

A Graph Path-Sum Framework for the Anderson Localization-Length Exponent from a Critical Green Function

We develop a graph path-sum framework for constructing determining equations for the Anderson localization-length exponent from a critical Green-function sector. The path sum is an all-depth representation; finite depth enters only as an explicit computational truncation. The critical Green function generates contact-i...

Yoshiki Ueoka · 0 citations
#edge computing Open access Sep 2026

A Graph Path-Sum Framework for the Anderson Localization-Length Exponent from a Critical Green Function

We develop a graph path-sum framework for constructing determining equations for the Anderson localization-length exponent from a critical Green-function sector. The path sum is an all-depth representation; finite depth enters only as an explicit computational truncation. The critical Green function generates contact-i...

Yoshiki Ueoka · 0 citations
#large language models Open access Sep 2026

Domain Number Mathematics I : Resolution as Problem Data, Dependency-Preserving Finite Relations, and FRN-2

Many mathematical problems are posed at a level of resolution stronger than what is required by a given task. This paper develops a framework in which requested mathematical resolution is treated as explicit problem data rather than only as a numerical-accuracy parameter. The contribution claimed here is not that inter...

Yoshiki Ueoka · 0 citations
#large language models Open access Sep 2026

Domain Number Mathematics I : Resolution as Problem Data, Dependency-Preserving Finite Relations, and FRN-2

Many mathematical problems are posed at a level of resolution stronger than what is required by a given task. This paper develops a framework in which requested mathematical resolution is treated as explicit problem data rather than only as a numerical-accuracy parameter. The contribution claimed here is not that inter...

Yoshiki Ueoka · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.