Hangzhou, China

Huaidong Xiong

USPTO Granted Patents = 1 

Average Co-Inventor Count = 4.0

ph-index = 1

Forward Citations = 1(Granted Patents)


Company Filing History:

goldMedal1 out of 832,912 
Other
 patents

Years Active: 2023

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1 patent (USPTO):Explore Patents

Title: Innovator Huaidong Xiong: Revolutionizing Model Training Methodologies

Introduction: Huaidong Xiong, a prominent inventor based in Hangzhou, China, holds a significant patent that showcases his innovative contributions to machine learning. His work focuses on enhancing model training methods and apparatus, addressing challenges in memory usage and training data efficiency.

Latest Patents: Xiong's notable patent, titled "Model training method and apparatus," provides a novel approach to model training. The patent outlines a method that includes reading a portion of sample data to create a sample subset, mapping model parameters from a first feature component to a second feature component, and training a model based on the processed data. This method effectively reduces the size of model parameters on a computer, significantly minimizing the amount of training data needed and optimizing memory usage during machine learning tasks.

Career Highlights: Although specific details regarding Huaidong Xiong's career path are limited, his contributions to the field of machine learning through his patent highlight his expertise and innovative spirit. With a focus on reducing resource overhead while maintaining efficiency, Xiong's work is poised to make a meaningful impact on the field.

Collaborations: Huaidong Xiong collaborates with talented individuals in the field, including coworkers Yi Ding and Jin Yu. Their combined efforts contribute to advancing research and development in machine learning and data processing methodologies.

Conclusion: Huaidong Xiong stands out as an influential inventor in the realm of machine learning and model training. His innovative patent not only signifies his contributions but also offers solutions to key challenges faced by researchers and developers. As technology continues to evolve, Xiong's work will undoubtedly inspire further advancements in efficient model training practices.

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