Gukyeong Kwon

Title: Gukyeong Kwon: Innovator in Neural Networks and Active Learning Frameworks

Introduction

Gukyeong Kwon is a notable inventor based in Atlanta, GA (US). He has made significant contributions to the field of neural networks and learning algorithms. His innovative approach focuses on enhancing active learning techniques through the use of gradient variance.

Latest Patents

Gukyeong Kwon holds a patent titled "Variance of gradient based active learning framework for training perception algorithms." This patent outlines a method where neural networks and learning algorithms utilize a variance of gradients to gain a heuristic understanding of the model. The process involves receiving a dataset with a vector, annotating it, and calculating a loss value. This loss is then used to update the neural network through backpropagation. An updated dataset allows for the calculation of additional losses, which are added to a pool of gradients. Ultimately, the variance of gradients is computed from this pool and used to further update the neural network.

Career Highlights

Gukyeong Kwon is currently employed at Ford Global Technologies, LLC, where he applies his expertise in neural networks and active learning. His work is instrumental in advancing the capabilities of perception algorithms.

Collaborations

Some of Gukyeong's coworkers include Armin Parchami and Ghassan AlRegib. Their collaborative efforts contribute to the innovative projects at Ford Global Technologies.

Conclusion

Gukyeong Kwon's work in developing a variance of gradient-based active learning frameworks showcases his commitment to advancing technology in neural networks. His contributions are paving the way for more efficient training of perception algorithms.