Los Angeles, CA, United States of America

Weijun Wang


Average Co-Inventor Count = 7.0

ph-index = 1

Forward Citations = 8(Granted Patents)


Company Filing History:


Years Active: 2021

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2 patents (USPTO):Explore Patents

Title: Weijun Wang: Innovator in Efficient Convolutional Neural Networks

Introduction

Weijun Wang is a prominent inventor based in Los Angeles, CA (US). He has made significant contributions to the field of artificial intelligence, particularly in the development of efficient convolutional neural networks. With a total of 2 patents, his work focuses on reducing computational costs associated with deep learning models.

Latest Patents

Wang's latest patents include innovative systems and methods aimed at minimizing the computational costs linked to convolutional neural networks. One of his notable contributions is the introduction of a class of efficient models known as 'MobileNets'. These models are designed for mobile and embedded vision applications. MobileNets utilize a straightforward architecture that incorporates depthwise separable convolutions to create lightweight deep neural networks. Furthermore, Wang's patents provide two global hyper-parameters that effectively balance latency and accuracy. This allows developers to select appropriately sized models tailored to specific application constraints. The techniques associated with MobileNets are versatile and applicable across a wide range of use cases.

Career Highlights

Weijun Wang is currently employed at Google Inc., where he continues to push the boundaries of innovation in artificial intelligence. His work has garnered attention for its practical applications and efficiency in computational processes.

Collaborations

Wang has collaborated with notable colleagues, including Andrew Gerald Howard and Bo Chen, who contribute to the advancement of technology in similar fields.

Conclusion

Weijun Wang's contributions to efficient convolutional neural networks and the development of MobileNets highlight his role as a key innovator in the tech industry. His work not only enhances the performance of AI applications but also addresses critical challenges in computational efficiency.

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