8 citations · 18 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
Flexible rational approximation and its application for matrix functions
Nir Sharon, Vinesha Peiris, Nadia Sukhorukova +1
This paper proposes a unique optimization approach for estimating the minimax rational approximation and its application for evaluating matrix functions. Our method enables the ext…
Deep Learning with Nonsmooth Objectives
Vinesha Peiris, Nadezda Sukhorukova, Vera Roshchina
We explore the potential for using a nonsmooth loss function based on the max-norm in the training of an artificial neural network. We hypothesise that this may lead to superior cl…
Multivariate approximation by polynomial and generalised rational functions
R. Díaz Millán, V. Peiris, N. Sukhorukova +1
In this paper we develop an optimisation based approach to multivariate Chebyshev approximation on a finite grid. We consider two models: multivariate polynomial approximation and…