Po-Wei Wang

Title: Po-Wei Wang: Innovator in Neural Networks and Data Learning

Introduction

Po-Wei Wang is a prominent inventor based in Pittsburgh, PA (US). He has made significant contributions to the fields of neural networks and data learning, holding a total of 5 patents. His innovative work focuses on optimizing neural network architectures and developing methods for extracting rules from databases.

Latest Patents

One of his latest patents is titled "Neural network with a layer solving a semidefinite program." This invention describes a system that applies a neural network to an input instance, incorporating an optimization layer that determines output neuron values based on input neuron values through joint optimization. The process involves obtaining input instances and computing output vectors by solving a semidefinite program defined by specific parameters.

Another notable patent is the "Method and system for learning rules from a database." This invention outlines a computer-implemented method for learning rules from a database containing entities and relations. The method includes deriving aggregate values from numerical and non-numerical relations, constructing differentiable operators, and extracting rules from these operators.

Career Highlights

Throughout his career, Po-Wei Wang has worked with esteemed organizations such as Robert Bosch GmbH and Carnegie Mellon University. His experience in these institutions has allowed him to refine his expertise in neural networks and data analysis.

Collaborations

He has collaborated with notable colleagues, including Jeremy Zico Kolter and Csaba Domokos. These partnerships have contributed to his innovative research and development efforts.

Conclusion

Po-Wei Wang's contributions to neural networks and data learning demonstrate his commitment to advancing technology in these fields. His patents reflect a deep understanding of complex systems and optimization techniques.