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.

本篇论文探讨了深度学习模型预测过程黑盒化的问题，并以作者的博士论文为基础，重点讨论了近年来备受关注的注意力机制及其在提高预测性能和可解释性方面的潜力，以及关于在大数据集和实际应用中评估和推广的应用研究，最终总结了这些发现对未来研究和发展的启迪。

通过注意机制从基础和应用研究角度改进预测性能和模型可解释性