How controllers from industrial machinery can coordinate multitask machine learning

Training a machine learning model to handle multiple objectives simultaneously is a bit like trying to follow GPS directions to several destinations at once: the routes often conflict, and compromising between them can leave you farther from every destination. A paper we presented at this year’s International Conference on Machine Learning (ICML) addresses this problem in the context of graph self-supervised learning (graph SSL), where the goal is to train a neural network to process graph data. Graph SSL objectives include inferring links between graph nodes, reconstructing nodes that have been masked out, and maximizing the mutual information between a given node and the nodes in its neighborhood. Our framework, ControlG, borrows an idea from industrial control systems: rather than blending all objectives together at every training step, it dedicates computational capacity to one objective at a time and lets a proportional-integral-derivative (PID) controller decide which objective needs attention next. Karish Grover, an Amazon PhD fellow, performed this work during an internship at Amazon Web Services,…