Speaker
Mr
Jose Miguel Munoz Arias
(EIA University)
Description
Besides modern architectures designed via geometric deep learning achieving high accuracies via Lorentz group invariance, this process involves high amounts of computation. Moreover, the framework is restricted to a particular classification scheme and lacks interpretability.
To tackle this issue, we present BIP, an efficient and computationally cheap framework to build rotational, permutation, and boost in the jet mean axis invariances. Moreover, we show the versatility of our approach to obtaining state-of-the-art range accuracies in both supervised and unsupervised jet tagging by using several out-of-the-box classifiers.
Primary authors
Mr
Christoph Ortner
(UBC Math Department)
Mr
Ilyes Batatia
(Engineering Laboratory, University of Cambridge)
Mr
Jose Miguel Munoz Arias
(EIA University)
