Caishi He

Title: Inventor Spotlight: Caishi He from Chongqing, CN

Introduction

Caishi He, an innovative inventor based in Chongqing, China, holds a notable patent in the field of neural network technologies. With expertise in developing cutting-edge systems, He has contributed significantly to advancements in quantitative analysis through his inventive approach. His work focuses on the intersection of technology and human-computer interaction, particularly in enhancing the stability and efficiency of line-of-sight estimation neural networks.

Latest Patents

Caishi He is credited with a patent titled "Quantitative analysis method and system for attention based on line-of-sight estimation neural network." This patent highlights a method that improves the training stability and efficiency of neural networks aimed at estimating line-of-sight. Through the implementation of a few-sample learning method, He enhances the generalization capabilities of the network. Additionally, the invention introduces a nonlinear division method for small angle intervals, which effectively reduces estimation errors. It also includes features for eye opening and closing detection, mitigating errors that can arise during eye closure. The method boasts high adaptability for real-world applications, displaying a quick deployment capability.

Career Highlights

Throughout his career, Caishi He has worked with prestigious institutions, reflecting his commitment to research and development. Notable organizations include Chongqing University, the Star Institute of Intelligent Systems, and the University of Electronic Science and Technology of China. His involvement in these esteemed establishments has allowed him to collaborate with fellow researchers and innovate across various domains.

Collaborations

Caishi He has partnered with notable colleagues in his field, including Yongduan Song. Their collaborative efforts have significantly impacted advancements in neural networks, focusing on improving performance metrics and real-world applications of emerging technologies.

Conclusion

Caishi He's contributions through his patent and collaborations underscore his role as a vital figure in the realm of innovations in neural network technologies. As advancements continue, his work is poised to influence future developments in how we interact with intelligent systems and enhance their functionality in various applications.