  With the dramatic advances in deep learning technology, machine learning
research is focusing on improving the interpretability of model predictions as
well as prediction performance in both basic and applied research. While deep
learning models have much higher prediction performance than traditional
machine learning models, the specific prediction process is still difficult to
interpret and/or explain. This is known as the black-boxing of machine learning
models and is recognized as a particularly important problem in a wide range of
research fields, including manufacturing, commerce, robotics, and other
industries where the use of such technology has become commonplace, as well as
the medical field, where mistakes are not tolerated. This bulletin is based on
the summary of the author's dissertation. The research summarized in the
dissertation focuses on the attention mechanism, which has been the focus of
much attention in recent years, and discusses its potential for both basic
research in terms of improving prediction performance and interpretability, and
applied research in terms of evaluating it for real-world applications using
large data sets beyond the laboratory environment. The dissertation also
concludes with a summary of the implications of these findings for subsequent
research and future prospects in the field.
