no code implementations • 13 Jan 2023 • Lorenzo Ciampiconi, Adam Elwood, Marco Leonardi, Ashraf Mohamed, Alessandro Rozza
This survey aims to provide a reference of the most essential loss functions for both beginner and advanced machine learning practitioners.
no code implementations • 12 Oct 2022 • Adam Elwood, Marco Leonardi, Ashraf Mohamed, Alessandro Rozza
This provides practitioners with new techniques that perform well in static and dynamic settings, and are particularly well suited to non-linear scenarios with continuous action spaces.
no code implementations • 29 Jul 2021 • Adam Elwood, Alberto Gasparin, Alessandro Rozza
With the rise in use of social media to promote branded products, the demand for effective influencer marketing has increased.
1 code implementation • 1 Jun 2018 • Adam Elwood, Dirk Krücker
We introduce two new loss functions designed to directly optimise the statistical significance of the expected number of signal events when training neural networks to classify events as signal or background in the scenario of a search for new physics at a particle collider.
High Energy Physics - Experiment