Jakob Konstantin Hecker

The Geometric Whitney Problem and Approximations by Neural Networks on Manifolds

If a dataset locally looks like a low-dimensional linear space – a condition testable directly from data and derivable from the empirically supported manifold hypothesis under well-behaved conditions – then an approximating manifold M can be constructed and neural networks can approximate C1 functions uniformly on M, w...