4 papers
Does the Barron space really defy the curse of dimensionality?
Olov Schavemaker
The Barron space has become famous in the theory of (shallow) neural networks because it seemingly defies the curse of dimensionality. And while the Barron space (and generalizatio…
On best approximation by multivariate ridge functions with applications to generalized translation networks
Paul Geuchen, Palina Salanevich, Olov Schavemaker +1
In this paper, we prove sharp upper and lower bounds for the approximation of Sobolev functions by sums of multivariate ridge functions, i.e., for approximation by functions of the…
Efficient uniform approximation using Random Vector Functional Link networks
Palina Salanevich, Olov Schavemaker
A Random Vector Functional Link (RVFL) network is a depth-2 neural network with random inner weights and biases. Only the outer weights of such an architecture are to be learned, s…
Separating balls with partly random hyperplanes with a view to partly random neural networks
Olov Schavemaker
We derive exact expressions for the probabilities that partly random hyperplanes separate two Euclidean balls. The probability that a fully random hyperplane separates two balls tu…