NIST · ИТ · cbb86cbeb70ecf12945b
DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks
Future advances in deep learning and its impact on the development of artificial intelligence (AI) in all fields depends heavily on data size and computational power. Sacrificing massive computing resources in exchange for better precision rates of the
NIST · ИТОфициальный источник · 2021-06-24
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"title_en": "DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks",
"designation": "",
"series": "",
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"date": "2021-06-24",
"date_original": "June 24, 2021",
"url": "https://www.nist.gov/publications/deepnetqoe-self-adaptive-qoe-optimization-framework-deep-networks",
"source": "NIST Publications",
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"authors_en": "Hamid Gharavi",
"abstract_excerpt_en": "Future advances in deep learning and its impact on the development of artificial intelligence (AI) in all fields depends heavily on data size and computational power. Sacrificing massive computing resources in exchange for better precision rates of the",
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{
"title_en": "DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks",
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"date": "2021-06-24",
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"url": "https://www.nist.gov/publications/deepnetqoe-self-adaptive-qoe-optimization-framework-deep-networks",
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"authors_en": "Hamid Gharavi",
"abstract_excerpt_en": "Future advances in deep learning and its impact on the development of artificial intelligence (AI) in all fields depends heavily on data size and computational power. Sacrificing massive computing resources in exchange for better precision rates of the"
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