Neuroevolution-based Automated Model Building: How to Create Better Models

Abstract: Common neural network architectures may work well for known, established data problems; however, they can fall short when modern machine learning applications demand more performance and higher levels of sophistication. In this session, Keith Moore, Director of Product Management at SparkCognition, covers how neural architecture search works, some of the challenges faced in the space, and why an evolutionary approach is capable of discovering sophisticated and elegant designs that fit your data. He will take you through the journey their team faced on building out better models, provide some areal-work examples and implementations, and also share some of the problems that are yet to be solved.

Bio: Keith Moore is the Director of Product Management at SparkCognition and is responsible for the development of the IoT product line (SparkPredict®). He specializes in applying advanced data science and natural language processing algorithms to complex data sets.
Moore previously worked for National Instruments as an analog-to-digital converter and vibration software product manager. Prior to that, he developed client software solutions for major oil and gas, aerospace, and semiconductor organizations.
Moore has served as a board member of Pi Kappa Phi fraternity, and still serves volunteers on the alumni engagement committee. He graduated from the University of Tennessee with a with a B.A. in mechanical engineering.