Company Filing History:
Years Active: 2024-2025
Title: Liyue Shen: Innovator in Neural Network Applications
Introduction
Liyue Shen is a prominent inventor based in Stanford, CA. He has made significant contributions to the field of neural networks, particularly in the area of image processing and reconstruction. With a total of 3 patents, his work has garnered attention for its innovative approaches to complex problems in medical imaging and data generation.
Latest Patents
One of Liyue Shen's latest patents is titled "Representational disentanglement for multi-modality image completion using neural networks." This invention focuses on generating data items related to an object from multiple data items using a generative adversarial network. The method involves disentangling data objects into common and unique components, which can then be used to train neural networks for generating missing data, such as medical images.
Another notable patent is "Implicit neural representation learning with prior embedding for sparsely sampled image reconstruction and other inverse problems." This method utilizes prior images to initialize neural network parameters, allowing for the reconstruction of images from sparse measurements. The training process minimizes the difference between the predicted intensity values and the actual measurements, resulting in improved diagnostic imaging.
Career Highlights
Liyue Shen has worked with esteemed institutions, including Leland Stanford Junior University and Nvidia Corporation. His experience in these organizations has allowed him to develop and refine his innovative techniques in neural networks and image processing.
Collaborations
Liyue has collaborated with notable individuals in his field, including Lei Xing and Wei Zhao. These partnerships have contributed to the advancement of his research and the successful development of his patents.
Conclusion
Liyue Shen's contributions to neural networks and image processing demonstrate his commitment to innovation in technology. His patents reflect a deep understanding of complex systems and a drive to improve medical imaging techniques. His work continues to influence the field and inspire future advancements.