DAE

Abstract

Computer vision is one of the many fields that successfully adopted machine learning for building predictive models. Yet, despite their success some of the fields' most popularly used models such as conditional random fields remain poorly understood theoretically and require approximations to be practical. I discuss a few of open theoretical and practical questions in these models in the computer vision context.