Speakers
Ben Nachman
(University of California Berkeley (US))
Eric Metodiev
(Massachusetts Institute of Technology)
Patrick Komiske
(Massachusetts Institute of Technology)
Description
Machine learning in high energy physics relies heavily on simulation for fully supervised training. This often results in sub-optimal classification when ultimately applied to (unlabeled) data. In addition to describing a new method for weak supervision (learning directly from data) called Classification Without Labels (CWoLa), we show for the first time how to apply these techniques to high-dimensional data, where significant architectural changes are required. This is critically important for learning from and about the full radiation pattern inside jets.
Primary authors
Ben Nachman
(University of California Berkeley (US))
Eric Metodiev
(Massachusetts Institute of Technology)
Patrick Komiske
(Massachusetts Institute of Technology)
Matthew Schwartz
