(Machine) Learning to Do More with Less

Understand

Determining the best method for training a machine learning algorithm is critical to maximizing its ability to classify data.

• In this paper, we compare the standard "fully supervised" approach (that relies on knowledge of event-by-event truth-level labels) with a recent proposal that instead utilizes class ratios as the only discriminating information provided during training.
• This so-called "weakly supervised" technique has access to less information than the fully supervised method and yet is still able to yield impressive discriminating power.
• In addition, weak supervision seems particularly well suited to particle physics since quantum mechanics is incompatible with the notion of mapping an individual event onto any single Feynman diagram.