Zhaopeng Tu

Title: Zhaopeng Tu: Innovator in Neural Network and Machine Translation Technologies

Introduction

Zhaopeng Tu is a prominent inventor based in Shenzhen, China, known for his significant contributions to the fields of neural networks and machine translation. With a total of 15 patents to his name, he has made remarkable advancements that enhance the efficiency and effectiveness of various computational methods.

Latest Patents

Among his latest patents, Zhaopeng Tu has developed a model training method, a machine translation method, a computer device, and a storage medium. These innovations disclose a neural network model training method that includes obtaining a training sample set with a standard tag vector. The process involves inputting the training sample into a neural network model with multiple attention networks to derive a feature fusion vector. The predicted tag vector is then obtained, and model parameters are adjusted until a convergence condition is met, resulting in a target neural network model. Additionally, he has created a sequence model processing method and apparatus aimed at improving task execution effects. This method includes encoding a source sequence using both a self-attention encoder and a temporal encoder, ultimately leading to a decoding result that enhances the overall performance of the sequence model.

Career Highlights

Zhaopeng Tu has worked with notable companies such as Tencent Technology (Shenzhen) Company Limited and Huawei Technologies Co., Limited. His experience in these leading technology firms has allowed him to refine his skills and contribute to groundbreaking innovations in the tech industry.

Collaborations

Some of his coworkers include Longyue Wang and Shuming Shi, who have collaborated with him on various projects, further enriching the innovative environment in which he operates.

Conclusion

Zhaopeng Tu's work in neural networks and machine translation exemplifies the spirit of innovation in technology. His patents not only reflect his expertise but also contribute significantly to advancements in computational methods.