Mathematical Modeling and Deep Learning for Small-Data AI

• Overview

  Current technologies of artificial intelligence (AI), including deep learning, are effective as long as we have many high-quality data where their quality is often measured by the “4V” of Big Data-Volume, Variety, Velocity, and Veracity. In contrast, we often encounter “Small Data” in physical space such as pandemic of disease spreading over social networks, biomedical information like EEG and fMRI, and sensor signals collected by numerous Internet-of-Things (IoT) devices. Discovering knowledge and extracting useful information from such Small Data are highly required to integrate cyber and physical spaces that results in a reliable and stable cyber-physical system. In this project, we study “Small-Data AI” that unifies mathematical modeling and deep learning from signal processing and machine learning perspectives.

  

Members

Toshiyuki Kondo 　(Institute of Engineering / Professor)
Akinobu Shimizu 　(Institute of Engineering / Professor)
Toshihisa Tanaka    (Institute of Engineering / Professor)
Kohei Yatabe   (Institute of Engineering / Associate Professor)