Shenzhen, China

Yuwei Wang

USPTO Granted Patents = 5 

 

Average Co-Inventor Count = 4.3

ph-index = 1

Forward Citations = 4(Granted Patents)


Location History:

  • Shenzhen, CN (2020 - 2022)
  • Guangdong, CN (2023)

Company Filing History:


Years Active: 2020-2023

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5 patents (USPTO):

Title: Yuwei Wang: Innovator in Convolutional Neural Networks

Introduction

Yuwei Wang is a prominent inventor based in Shenzhen, China. He has made significant contributions to the field of convolutional neural networks, particularly through his innovative pooling processing methods. With a total of 5 patents, Wang's work has advanced the efficiency of data processing in neural networks.

Latest Patents

Wang's latest patents include a pooling processing method and system applied to convolutional neural networks. This application discloses a method that involves receiving a feature map with width and height dimensions. The method establishes a data region and transfers data blocks from the feature map into this region in a sequential scanning order. It retains previous pooling cycle data and performs first and second level pooling operations to enhance processing efficiency. The technical solution reduces storage needs and improves the overall efficiency of pooling processing.

Career Highlights

Throughout his career, Yuwei Wang has worked with notable companies, including Tencent Technology (Shenzhen) Company Limited and Tencent Technology Company Limited. His experience in these organizations has allowed him to refine his skills and contribute to significant advancements in technology.

Collaborations

Wang has collaborated with several professionals in his field, including Xiaoyu Yu and Lixin Zhang. These collaborations have fostered innovation and have been instrumental in the development of his patented technologies.

Conclusion

Yuwei Wang's contributions to convolutional neural networks through his innovative patents and career achievements highlight his role as a leading inventor in the technology sector. His work continues to influence advancements in data processing and neural network efficiency.

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