5 papers
KAN vs LSTM Performance in Time Series Forecasting
Tabish Ali Rather, S M Mahmudul Hasan Joy, Nadezda Sukhorukova +1
This study presents a controlled comparison of baseline Kolmogorov-Arnold Networks (KAN), implemented via PyKAN, and Long Short-Term Memory (LSTM) networks for the forecasting of s…
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…
Bivariate rational approximations of the general temperature integral
Alireza Aghili, Nadezda Sukhorukova, Julien Ugon
The non-isothermal analysis of materials with the application of the Arrhenius equation involves temperature integration. If the frequency factor in the Arrhenius equation depends…