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Talk
Title	Neural networks for the abstraction of the physical symmetries in the nature
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Author(s)	Cho, Wonsang (speaker) (Seoul National University)
Corporate author(s)	CERN. Geneva
Imprint	2019-04-16. - 0:22:01.
Series	(LPCC Workshops) (3rd IML Machine Learning Workshop)
Lecture note	on 2019-04-16T10:05:00
Subject category	LPCC Workshops
Abstract	Neural networks are so powerful universal approximator of complicated patterns in large-scale data, leading the explosive developments of AI in terms of deep learning. However, in many cases, usual neural networks are trained to possess poor level of abstraction, so that the model's predictability and generalizability can be quite unstable, depending on the quality and amount of the data used for training. In this presentation, we introduce a new neural network architecture which has improved capability of capturing the key features and the physical laws hidden in data, in a mathematically more robust and simpler way. We demonstrate the performance of the new architecture, with an application for high energy particle scattering processes at the LHC.
Copyright/License	© 2019-2024 CERN
Submitted by	paul.seyfert@cern.ch