8 citations · 18 across the 9 of their papers we have counts for
11 papers
Difference of Convex (DC) approach for neural network approximation with uniform loss function
Vinesha Peiris, Nadezda Sukhorukova
Neural networks (NNs) can be viewed as approximation tools. Traditionally, NNs are relying on gradient and stochastic gradient (SG) methods. There are a number of available computa…
KKT-based optimality conditions for neural network approximation
Vinesha Peiris, Nadezda Sukhorukova, Julien Ugon
In this paper, we obtain necessary optimality conditions for neural network approximation. We consider neural networks in Manhattan ( norm) and Chebyshev ( norm). The op…
Nonsmooth Optimisation and neural networks
Vinesha Peiris, Nadezda Sukhorukova
In this paper, we study neural networks from the point of view of nonsmooth optimisation, namely, quasidifferential calculus. We restrict ourselves to the case of uniform approxima…
Best free knot linear spline approximation and its application to neural networks
Vinesha Peiris, Duy Khoa Pham, Nadezda Sukhorukova
The problem of fixed knot approximation is convex and there are several efficient approaches to solve this problem, yet, when the knots joining the affine parts are also variable,…
A comparison of rational and neural network based approximations
Vinesha Peiris, Reinier Diaz Millan, Nadezda Sukhorukova +1
Rational and neural network based approximations are efficient tools in modern approximation. These approaches are able to produce accurate approximations to nonsmooth and non-Lips…
Rational activation functions in neural networks with uniform based loss functions and its application in classification
Vinesha Peiris
In this paper, we demonstrate the application of generalised rational uniform (Chebyshev) approximation in neural networks. In particular, our activation functions are one degree r…