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Title: Machine Learning Adversarial Label Tampering: Design and Detection.

Abstract

Abstract not provided.

Authors:
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1513074
Report Number(s):
SAND2018-0632C
662977
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the AI Forum held February 5-6, 2018 in Baltimore, Maryland.
Country of Publication:
United States
Language:
English

Citation Formats

Kegelmeyer, W. Philip. Machine Learning Adversarial Label Tampering: Design and Detection.. United States: N. p., 2018. Web.
Kegelmeyer, W. Philip. Machine Learning Adversarial Label Tampering: Design and Detection.. United States.
Kegelmeyer, W. Philip. Mon . "Machine Learning Adversarial Label Tampering: Design and Detection.". United States. https://www.osti.gov/servlets/purl/1513074.
@article{osti_1513074,
title = {Machine Learning Adversarial Label Tampering: Design and Detection.},
author = {Kegelmeyer, W. Philip},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

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