Yunlong He

Title: Innovations of Yunlong He

Introduction

Yunlong He is an accomplished inventor based in Princeton, NJ (US). He has made significant contributions to the field of computer science, particularly in the area of latent factor models. His work focuses on developing frameworks that enhance the understanding and representation of high-dimensional data.

Latest Patents

Yunlong He holds a patent titled "Latent factor dependency structure determination." This patent discloses a general learning framework for computer implementation that induces sparsity on the undirected graphical model imposed on the vector of latent factors. The latent factor model, SLFA, is presented as a matrix factorization problem with a special regularization term that encourages collaborative reconstruction. This innovative model allows for the simultaneous learning of lower-dimensional representations for data while explicitly modeling the pairwise relationships between latent factors. An on-line learning algorithm is also introduced, making the model suitable for large-scale learning problems. Experimental results on synthetic and real-world data sets demonstrate that the learned representations achieve state-of-the-art classification performance.

Career Highlights

Yunlong He is currently employed at NEC Laboratories America, Inc. His work at this organization has allowed him to further his research and development in advanced computational models. His contributions have been recognized within the industry, showcasing his expertise in the field.

Collaborations

Yunlong has collaborated with notable colleagues, including Yanjun Qi and Koray Kavukcuoglu. These partnerships have fostered a collaborative environment that enhances innovation and research outcomes.

Conclusion

Yunlong He is a prominent inventor whose work in latent factor models has significantly advanced the field of computer science. His innovative approaches and collaborations continue to influence the way high-dimensional data is explored and understood.