15-18 April 2019
CERN
Europe/Zurich timezone
There is a live webcast for this event.

Neural networks for the abstraction of the physical symmetries in the nature

16 Apr 2019, 10:05
20m
500-1-001 - Main Auditorium (CERN)

500-1-001 - Main Auditorium

CERN

400
Show room on map

Speaker

Wonsang Cho (Seoul National University)

Description

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.

Preferred contribution length 20 minutes

Primary author

Wonsang Cho (Seoul National University)

Presentation Materials

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