A non-asymptotic bound on compression error for signal parameterizations derived from differences in predictions at varying compression levels, verified empirically across fitting and inverse problems.
Optimizing deep learning models for on-orbit deployment through neural architecture search
2 Pith papers cite this work. Polarity classification is still indexing.
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Onboard processing in IRIDE HEO complements ground-based EO services by enabling sub-3m resolution burnt-area mapping, 3-hectare minimum mapping units, and improved responsiveness for emergency workflows.
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Bounding Global and Local Compression Error of Signal Parameterizations
A non-asymptotic bound on compression error for signal parameterizations derived from differences in predictions at varying compression levels, verified empirically across fitting and inverse problems.
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Assessing the Added Value of Onboard Earth Observation Processing with the IRIDE HEO Service Segment
Onboard processing in IRIDE HEO complements ground-based EO services by enabling sub-3m resolution burnt-area mapping, 3-hectare minimum mapping units, and improved responsiveness for emergency workflows.