San Jose, CA, United States of America

Bingyue Peng

USPTO Granted Patents = 1 

Average Co-Inventor Count = 11.0

ph-index = 1

Forward Citations = 10(Granted Patents)


Company Filing History:


Years Active: 2022

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1 patent (USPTO):Explore Patents

Title: The Innovative Mind of Bingyue Peng

Introduction

Bingyue Peng is a notable inventor based in San Jose, California. He has made significant contributions to the field of machine learning through his innovative patent. His work focuses on automating the generation of machine learning models, which has the potential to streamline processes in various applications.

Latest Patents

Bingyue Peng holds a patent for the "Automated generation of machine learning models." This patent describes a method where machine-trained models are generated based on a model description that defines parameters for training. The unique aspect of this invention is that it allows model descriptions to inherit parameters from parent model descriptions. When changes are made to a parent model description, these changes are automatically applied to the child model description. The process involves identifying the parent model from a received description and retrieving the necessary parameters to generate the target model. This innovative pipeline enhances the efficiency of generating machine learning models.

Career Highlights

Bingyue Peng is currently employed at Meta Platforms, Inc., where he continues to push the boundaries of technology and innovation. His work at Meta has allowed him to collaborate with other talented individuals in the field, contributing to advancements in machine learning.

Collaborations

Some of Bingyue Peng's coworkers include Jurgen Anne Francois Marie Van Gael and Yu Ning. Their collaborative efforts contribute to the innovative environment at Meta Platforms, Inc.

Conclusion

Bingyue Peng's contributions to machine learning through his patent demonstrate his innovative spirit and dedication to advancing technology. His work not only enhances the efficiency of model generation but also showcases the potential for future developments in the field.

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