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Optimal approximation of discrete random variables
The dataset used in the paper is a discrete random variable X and a number m. -
Non-asymptotic approximations of neural networks by Gaussian processes
The dataset is not explicitly described in the paper, but it is mentioned that the authors study the extent to which wide neural networks may be approximated by Gaussian processes. -
A Discrete Tchebichef Transform Approximation for Image and Video Coding
A low-complexity approximation for the 8-point DTT was proposed. The arithmetic cost of the proposed approximation are significantly low, when compared with the exact DTT. At... -
Algebraic function based Banach space valued ordinary and fractional neural n...
The dataset is used for testing the univariate quantitative approximation, ordinary and fractional, of Banach space valued continuous functions on a compact interval or all the...
