EPJ Web Conf.
Volume 137, 2017XIIth Quark Confinement and the Hadron Spectrum
|Number of page(s)||9|
|Section||Statistical Methods for Physics Analysis in the XXI Century|
|Published online||22 March 2017|
Deep Learning and Bayesian Methods
Department of Physics, Florida State University, Tallahassee, FL 32306 USA
a e-mail: firstname.lastname@example.org
Published online: 22 March 2017
A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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