Recently, graph neural networks and graph structure learning, which can excellently learn complex time series relationships, have been applied to multivariate time series. However, existing research ...

Fitting of multivariate functions Abstract: This research presents a nonparametric algorithm for fitting of a multivariate function from noisy data. Conditions are provided that assure local and ...

GRIN is a graph-based, bidirectional, recurrent neural network which aims to reconstruct the input sequence by leveraging on both the temporal and spatial dimensions. GRIN reconstructs missing data in ...

MATLAB/OCTAVE functions for the Polynomial Numerical Linear Algebra Framework - stepan-a/system-of-multivariate-polynomials ...

A fundamental step of any quantum algorithm is the preparation of qubit registers in a suitable initial state. Often qubit registers represent a discretization of continuous variables and the initial ...

Variations and improvements to the standard KAN architecture are also explored, such as graph-based designs, convolutional KANs, and transformer-based KANs to solve the issues. Moreover, alternative ...

Spread the loveFinding the extrema of a multivariable function is an essential skill in calculus and applied mathematics. This process allows you to determine the minima and maxima of a function ...

Sketch derived, inverse or other related functions using graph translations. Complete the square and find composite functions for Higher Maths.