On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case

3 Feb 2020Giles Chatham Strong

Beginning from a basic neural-network architecture, we test the potential benefits offered by a range of advanced techniques for machine learning, in particular deep learning, in the context of a typical classification problem encountered in the domain of high-energy physics, using a well-studied dataset: the 2014 Higgs ML Kaggle dataset. The advantages are evaluated in terms of both performance metrics and the time required to train and apply the resulting models... (read more)

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