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Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks

Araz, Jack Y. and Spannowsky, Michael (2021) 'Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks.', Journal of High Energy Physics, 2021 (4). p. 296.

Abstract

Ensemble learning is a technique where multiple component learners are combined through a protocol. We propose an Ensemble Neural Network (ENN) that uses the combined latent-feature space of multiple neural network classifiers to improve the representation of the network hypothesis. We apply this approach to construct an ENN from Convolutional and Recurrent Neural Networks to discriminate top-quark jets from QCD jets. Such ENN provides the flexibility to improve the classification beyond simple prediction combining methods by linking different sources of error correlations, hence improving the representation between data and hypothesis. In combination with Bayesian techniques, we show that it can reduce epistemic uncertainties and the entropy of the hypothesis by simultaneously exploiting various kinematic correlations of the system, which also makes the network less susceptible to a limitation in training sample size.

Item Type:Article
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Available under License - Creative Commons Attribution 4.0.
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1007/JHEP04(2021)296
Publisher statement:Open Access. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.
Date accepted:02 April 2021
Date deposited:28 July 2021
Date of first online publication:30 April 2021
Date first made open access:28 July 2021

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