Konstantin Häberle

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.