In an ever expanding set of research and application areas, deep neural networks (DNNs)
set the bar for algorithm performance. However, depending upon additional constraints such
as processing power and execution time limits, or requirements such as verifiable safety
guarantees, it may not be feasible to actually use such high-performing DNNs in practice.
Many techniques have been developed in recent years to compress or distill complex DNNs
into smaller, faster or more understandable models and controllers. This work seeks to …