Abstract: 

Is your Generative adversarial neural network (GANs) not producing representative synthetic data? If yes, that is no surprise because training a GAN to produce quality data representative of the natural distributions is more complex than traditional predictive modeling. Ensuring the data is representative often requires an analysis of the covariate relationships and a comparison of the moments in the synthetic and natural (actual) distributions. This presentation will detail how a genetic algorithm can be combined with a set pseudo discriminators to automate constructing a better GAN.

Bio: 

Robert is a Principal Data Scientist at SAS where he builds end-to-end artificial intelligence applications. He also researches, consults, and teaches machine learning with an emphasis on deep learning and computer vision for SAS. Robert has authored an introductory book on computer vision and has written several professional courses on topics including neural networks, deep learning, and optimization modeling. Before joining SAS, Robert worked under the Senior Vice Provost at North Carolina State University, where he built models pertaining to student success, faculty development, and resource management. Prior to working in academia, Robert was a member of the research and development group on the Workforce Optimization team at Travelers Insurance. His models at Travelers focused on forecasting and optimizing resources. Robert graduated with a master’s degree in Business Analytics and Project Management from the University of Connecticut and a master’s degree in Applied and Resource Economics from East Carolina University.