About: Training robust anomaly detection using ML-Enhanced simulations

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Training robust anomaly detection using ML-Enhanced simulations
Creator	Feldman, Philip
source	ArXiv
abstract	This paper describes the use of neural networks to enhance simulations for subsequent training of anomaly-detection systems. Simulations can provide edge conditions for anomaly detection which may be sparse or non-existent in real-world data. Simulations suffer, however, by producing data that is%22too clean%22resulting in anomaly detection systems that cannot transition from simulated data to actual conditions. Our approach enhances simulations using neural networks trained on real-world data to create outputs that are more realistic and variable than traditional simulations.
has issue date	2020-08-27(xsd:dateTime)
has license	arxiv
sha1sum (hex)	6b1f2671166eaeaf42d53e12ac8e21a8e782b3ea
resource representing a document's title	Training robust anomaly detection using ML-Enhanced simulations
resource representing a document's body	covid:6b1f2671166eaeaf42d53e12ac8e21a8e782b3ea#body_text
is schema:about of	named entity 'anomaly detection' named entity 'paper' named entity 'Alaska Native' named entity 'SYSTEMS' named entity 'USE OF' »more»

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