Company Filing History:
Years Active: 2016-2022
Title: Innovations of Xiangqian Hu in Neural Network Training Systems
Introduction
Xiangqian Hu is a prominent inventor based in Cary, NC (US), known for his contributions to the field of machine learning and neural networks. With a total of 8 patents to his name, Hu has made significant strides in developing advanced computing devices that enhance the training of neural network models.
Latest Patents
Among his latest innovations is a neural network training system that utilizes a computing device to train a neural network machine learning model. This system executes a forward propagation of a first neural network, followed by a backward propagation from the last layer to the last convolution layer to compute a gradient vector. Additionally, it computes a discriminative localization map for each observation vector using the computed gradient vector. The system selects an activation threshold value for each observation vector based on a prediction error and computes a biased feature map accordingly. Furthermore, it generates a masked observation vector using the biased feature map and executes a predefined number of iterations of forward and backward propagation of a second neural network using this masked observation vector.
Career Highlights
Xiangqian Hu is currently employed at SAS Institute Inc., where he continues to innovate and contribute to the field of artificial intelligence. His work focuses on improving the efficiency and accuracy of neural network training systems, which are crucial for various applications in technology and data analysis.
Collaborations
Hu collaborates with notable colleagues such as Xinmin Wu and Guixian Lin, who share his passion for advancing machine learning technologies. Their combined expertise fosters an environment of innovation and creativity within their projects.
Conclusion
Xiangqian Hu's contributions to neural network training systems exemplify the impact of innovative thinking in technology. His patents reflect a commitment to enhancing machine learning processes, paving the way for future advancements in the field.
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