13 citations · 31 across the 7 of their papers we have counts for
7 papers
A two-phase model of collective memory decay with a dynamical switching point
Naoki Igarashi, Yukihiko Okada, Hiroki Sayama +1
Public memories of significant events shared within societies and groups have been conceptualized and studied as collective memory since the 1920s. Thanks to the recent advancement…
Non-readily identifiable data collaboration analysis for multiple datasets including personal information
Akira Imakura, Tetsuya Sakurai, Yukihiko Okada +3
Multi-source data fusion, in which multiple data sources are jointly analyzed to obtain improved information, has considerable research attention. For the datasets of multiple medi…
Another Use of SMOTE for Interpretable Data Collaboration Analysis
Akira Imakura, Masateru Kihira, Yukihiko Okada +1
Recently, data collaboration (DC) analysis has been developed for privacy-preserving integrated analysis across multiple institutions. DC analysis centralizes individually construc…
Collaborative causal inference on distributed data
Yuji Kawamata, Ryoki Motai, Yukihiko Okada +2
In recent years, the development of technologies for causal inference with privacy preservation of distributed data has gained considerable attention. Many existing methods for dis…
Application of Particle Swarm Optimization method to On-going Monitoring for estimating vehicle-bridge interaction system
Kyosuke Yamamoto, Kakeru Murakami, Ryota Shin +1
This study proposes a method for estimating the mechanical parameters of vehicles and bridges and the road unevenness, using only vehicle vibration and position data. In the propos…
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…