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Scattering networks can achieve optimal separation capacity by strategically tuning filter frequencies and ensuring well-conditioned geometric couplings.
Refining Cover's theory reveals that low-dimensional data structures can dramatically enhance classification capabilities, challenging traditional assumptions in machine learning.
Identifiability bounds reveal the precise conditions under which distinct ODEs can be distinguished from solution data, transforming our understanding of equation recovery in scientific machine learning.
A single fixed RNN can achieve any desired accuracy for continuous functions by simply running longer, challenging the need for new networks with improved target accuracy.
