10 citations · 22 across the 8 of their papers we have counts for
12 papers
Complex moment-based method with nonlinear transformation for computing large and sparse interior singular triplets
Akira Imakura, Tetsuya Sakurai
This paper considers computing interior singular triplets corresponding to the singular values in some interval. Based on the concept of the complex moment-based parallel eigensolv…
Accuracy and Privacy Evaluations of Collaborative Data Analysis
Akira Imakura, Anna Bogdanova, Takaya Yamazoe +2
Distributed data analysis without revealing the individual data has recently attracted significant attention in several applications. A collaborative data analysis through sharing…
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations
Anna Bogdanova, Akie Nakai, Yukihiko Okada +2
Dimensionality Reduction is a commonly used element in a machine learning pipeline that helps to extract important features from high-dimensional data. In this work, we explore an…
Interpretable collaborative data analysis on distributed data
Akira Imakura, Hiroaki Inaba, Yukihiko Okada +1
This paper proposes an interpretable non-model sharing collaborative data analysis method as one of the federated learning systems, which is an emerging technology to analyze distr…
Flexible subspace iteration with moments for an effective contour integration-based eigensolver
Sarah Huber, Yasunori Futamura, Martin Galgon +3
Contour integration schemes are a valuable tool for the solution of difficult interior eigenvalue problems. However, the solution of many large linear systems with multiple right h…
A Parallel Computing Method for the Coupled-Cluster Singles and Doubles
Takumi Yamashita, Taichi Kosugi, Yu-ichiro Matsushita +1
In this paper, we present a parallel computing method for the coupled-cluster singles and doubles (CCSD) in periodic systems. The CCSD in periodic systems solves simultaneous equat…