Hangzhou, China

Chengping Yang


 

Average Co-Inventor Count = 4.0

ph-index = 1


Company Filing History:


Years Active: 2022-2023

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2 patents (USPTO):Explore Patents

Title: Chengping Yang: Innovator in Neural Network Technologies

Introduction

Chengping Yang is a prominent inventor based in Hangzhou, China. He has made significant contributions to the field of artificial intelligence, particularly in the development of methods for updating convolutional neural networks. With a total of 2 patents to his name, Yang's work is at the forefront of technological innovation.

Latest Patents

Yang's latest patents include a method, apparatus, and device for updating convolutional neural networks using GPU clusters. This innovative method involves obtaining a sample with a classification label and performing a series of operations to achieve a classification result. The process includes determining a prediction error and updating the convolutional neural network based on this error. Another notable patent is focused on trusted execution environment-based model training methods and apparatuses. This method emphasizes the importance of security in model training by utilizing encrypted target samples and a trusted execution environment for processing.

Career Highlights

Throughout his career, Chengping Yang has worked with notable companies such as Advanced New Technologies Co., Ltd. and Alipay (Hangzhou) Information Technology Co., Ltd. His experience in these organizations has allowed him to refine his skills and contribute to groundbreaking projects in the tech industry.

Collaborations

Yang has collaborated with talented individuals in his field, including Yongchao Liu and Qiyin Huang. These partnerships have fostered an environment of innovation and creativity, leading to advancements in their respective areas of expertise.

Conclusion

Chengping Yang's contributions to neural network technologies and his innovative patents highlight his role as a key figure in the field of artificial intelligence. His work continues to influence the development of secure and efficient machine learning methods.

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