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pro vyhledávání: '"Shaowang, Ted"'
Video analytics systems based on deep learning models are often opaque and brittle and require explanation systems to help users debug. Current model explanation system are very good at giving literal explanations of behavior in terms of pixel contri
Externí odkaz:
http://arxiv.org/abs/2405.17686
Autor:
Liu, Shinan, Shaowang, Ted, Wan, Gerry, Chae, Jeewon, Marques, Jonatas, Krishnan, Sanjay, Feamster, Nick
Network traffic analysis increasingly uses complex machine learning models as the internet consolidates and traffic gets more encrypted. However, over high-bandwidth networks, flows can easily arrive faster than model inference rates. The temporal na
Externí odkaz:
http://arxiv.org/abs/2402.03694
Autor:
Shaowang, Ted, Krishnan, Sanjay
The relevant features for a machine learning task may arrive as one or more continuous streams of data. Serving machine learning models over streams of data creates a number of interesting systems challenges in managing data routing, time-synchroniza
Externí odkaz:
http://arxiv.org/abs/2303.08028
Autor:
Shaowang, Ted, Krishnan, Sanjay
The relevant features for a machine learning task may be aggregated from data sources collected on different nodes in a network. This problem, which we call decentralized prediction, creates a number of interesting systems challenges in managing data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fcd30c8bdcb75d92c817bd5fc5019970
http://arxiv.org/abs/2303.08028
http://arxiv.org/abs/2303.08028
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