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Apr 21, 2020 · Abstract: Machine learning techniques have become pervasive through many technical fields but an obstacle for employment is often the criterion ...
Apr 20, 2020 · Robustness can have many facets; some of them are covered by this timely special issue that represents the state of the art from a design and ...
From the EIC: Robust Machine Learning. https://doi.org/10.1109/mdat.2020.2984228. Journal: IEEE Design & Test, 2020, № 2, p. 4-4.
Special Issue on "Robust Machine Learning". Keynote by Sanjit A. Seshia, Somesh Jha, and Tommaso Dreossi "Semantic Adversarial Deep Learning ".
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This book explains in simple terms what it means for a distributed machine learning scheme to be robust to these threats, and how to build provably robust ...
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May 25, 2024 · This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art ...
Principles and methods for Robust ML; Quantification and verification of Robust ML; Applications in health, environmental sciences, design, autonomous vehicles, ...
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May 28, 2021 · Here we demonstrate that combining a data-driven methodology with some general physical principles enables discovery of a quantitatively accurate model.
Federated learning enables multiple medical institutions to jointly train and validate state-of-the-art machine learning models to improve patient outcomes ...
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Robust machine learning typically refers to the robustness of machine learning algorithms. For a machine learning algorithm to be considered robust, ...
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