117 citations · 442 across the 44 of their papers we have counts for
5 papers · 1 filter
Dimension independent bounds for general shallow networks
Hrushikesh N. Mhaskar
This paper proves an abstract theorem addressing in a unified manner two important problems in function approximation: avoiding curse of dimensionality and estimating the degree of…
A direct approach for function approximation on data defined manifolds
Hrushikesh Mhaskar
In much of the literature on function approximation by deep networks, the function is assumed to be defined on some known domain, such as a cube or a sphere. In practice, the data…
Super-resolution meets machine learning: approximation of measures
H. N. Mhaskar
The problem of super-resolution in general terms is to recuperate a finitely supported measure given finitely many of its coefficients with respect to some orthonorm…
Function approximation by deep networks
H. N. Mhaskar, T. Poggio
We show that deep networks are better than shallow networks at approximating functions that can be expressed as a composition of functions described by a directed acyclic graph, be…
A witness function based construction of discriminative models using Hermite polynomials
H. N. Mhaskar, A. Cloninger, X. Cheng
In machine learning, we are given a dataset of the form , drawn as i.i.d. samples from an unknown probability distribution ; the marginal distrib…