Paired Training Framework for Time-Constrained Learning
Jung-Eun Kim1,a, Richard Bradford2, Max Del Giudice1,b and Zhong Shao1,c
1Computer Science Yale University New Haven, CT, USA
ajung-eun.kim@yale.edu
bmax.delgiudice@yale.edu
czhong.shao@yale.edu
2Commercial Avionics Engineering Collins Aerospace Cedar Rapids, IA, USA
richard.bradford@collins.com
ABSTRACT
This paper presents a design framework for machine learning applications that operate in systems such as cyber-physical systems where time is a scarce resource. We manage the tradeoff between processing time and solution quality by performing as much preprocessing of data as time will allow. This approach leads us to a design framework in which there are two separate learning networks: one for preprocessing and one for the core application functionality. We show how these networks can be trained together and how they can operate in an anytime fashion to optimize performance.
