Jul 17, 2020 · In this work we discuss the impact of nuisance parameters on the effectiveness of machine learning in high-energy physics problems.

This work discusses the impact of nuisance parameters on the effectiveness of machine learning in high-energy physics problems, and provides a review of ...

In this work we discuss the impact of nuisance parameters on the effectiveness of machine learning in high-energy physics problems, and provide a review of ...

Jul 17, 2020 · In this work we discuss the impact of nuisance parameters on the effectiveness of machine learning in high-energy physics problems, and ...

We will discuss the impact of nuisance parameters on HEP analyses and how to reduce it by focusing on supervised classification, which is by far the most.

Tommaso Dorigo, Pablo de Castro: Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review. CoRR abs/2007.09121 (2020).

Jan 17, 2021 · In this work we discuss the impact of nuisance parameters on the ef- fectiveness of machine learning in high-energy physics problems, and ...

The approaches discussed include nuisance-parametrized models, modified or adversary losses, semi-supervised learning approaches and inference-aware techniques.

Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review. Dorigo, Tommaso, de Castro, Pablo. Jul-17-2020 –arXiv.org Machine ...

Modern machine learning techniques, including deep learning, is rapidly being applied, adapted, and developed for high energy physics. The goal of this document ...