NIST · ИТ · 923d660305ac8756e38e
Leveraging Combinatorial Coverage in the Machine Learning Product Lifecycle
The data-intensive nature of machine learning (ML)-enabled systems introduces unique challenges in test and evaluation. We present an overview of combinatorial coverage, exploring its applications across the ML-enabled system lifecycle and its potential to
NIST · ИТОфициальный источник · 2024-06-27
{
"title_en": "Leveraging Combinatorial Coverage in the Machine Learning Product Lifecycle",
"designation": "",
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"date": "2024-06-27",
"date_original": "June 27, 2024",
"url": "https://www.nist.gov/publications/leveraging-combinatorial-coverage-machine-learning-product-lifecycle",
"source": "NIST Publications",
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"authors_en": "Jaganmohan Chandrasekaran, erin lanus, tyler cody, laura freeman, Raghu N. Kacker , M S Raunak , D. Richard Kuhn",
"abstract_excerpt_en": "The data-intensive nature of machine learning (ML)-enabled systems introduces unique challenges in test and evaluation. We present an overview of combinatorial coverage, exploring its applications across the ML-enabled system lifecycle and its potential to",
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"title_en": "Leveraging Combinatorial Coverage in the Machine Learning Product Lifecycle",
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"date": "2024-06-27",
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"url": "https://www.nist.gov/publications/leveraging-combinatorial-coverage-machine-learning-product-lifecycle",
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"authors_en": "Jaganmohan Chandrasekaran, erin lanus, tyler cody, laura freeman, Raghu N. Kacker , M S Raunak , D. Richard Kuhn",
"abstract_excerpt_en": "The data-intensive nature of machine learning (ML)-enabled systems introduces unique challenges in test and evaluation. We present an overview of combinatorial coverage, exploring its applications across the ML-enabled system lifecycle and its potential to"
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