PHYSTAT

Sticking to the roots of machine learning: old-school approaches to physics analysis

Name: Sticking to the roots of machine learning: old-school approaches to physics analysis
Start: 2022-06-15T16:00:00+02:00
End: 2022-06-15T17:30:00+02:00
Location: No location set

by Tommaso Dorigo (Universita e INFN, Padova (IT))

Wednesday 15 Jun 2022, 16:00 → 17:30 Europe/Zurich

Description

In this seminar I will take a step back from the recent trend of DNNizing everything, by discussing the merits of two recent results, produced using statistical learning approaches to supervised and unsupervised learning problems of interest to HEP. The first is the use of a 65-million-parameters k-NN algorithm for deep regression [1]. The second is the identification of anomalies in HEP data in a fully unsupervised way, exploiting the random sampling of their copula space [2].

Organised by

O. Behnke, L. Lyons, L. Moneta, N. Wardle

68793225561

Olaf Behnke

Nicholas Wardle

07630691

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Sticking to the roots of machine learning: old-school approaches to physics analysis

by Tommaso Dorigo (Universita e INFN, Padova (IT))