Contraction Theory for Machine Learning - YouTube

Contraction Theory for Machine Learning

Contraction Theory for Machine Learning

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Tutorial Session at 60th IEEE Conference on Decision and Control (CDC)

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Deep MinMax Networks

W. Lohmiller (MIT), P. Gassert (TU Munich), J.-J. Slotine (MIT)

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Safe Motion Planning with Tubes and Contraction Metrics

S. Singh (Google), H. Tsukamoto (Caltech), B. Lopez (UCLA), S.-J. Chung (Caltech), J.-J. Slotine (MIT)

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Contraction-based Methods for Stable Identification and Robust Machine Learning: A Tutorial

I. R. Manchester (U of Sydney), M. Revay (U of Sydney), R. Wang (U of Sydney)

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