In:
Journal of Instrumentation, IOP Publishing, Vol. 17, No. 08 ( 2022-08-01), p. T08013-
Abstract:
We compare different neural network architectures for
machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The
architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package
“Multi-node Evolutionary Neural Networks for Deep Learning” (MENNDL), developed at Oak Ridge National Laboratory. While the
domain-expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks
performed as well. There is always a trade-off between human, and computer resources for network optimization and this work suggests
that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.
Type of Medium:
Online Resource
ISSN:
1748-0221
DOI:
10.1088/1748-0221/17/08/T08013
Language:
Unknown
Publisher:
IOP Publishing
Publication Date:
2022
detail.hit.zdb_id:
2235672-1
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