This paper proposes a novel methodology to detect Granger causality in mean in vector autoregressive settings using feedforward neural networks. The approach accommodates unknown dependence structures between the elements of highly-dimensional multivariate time series with weak and strong...

Building on an economic model of rational Bitcoin mining, we measure the carbon footprint of Bitcoin mining power consumption using feed-forward neural networks. After reviewing the literature on deep learning methods, we find associated carbon footprints of 3.8038, 23.8313 and 19.83472 MtCOe...

We propose an optimal architecture for deep neural networks of given size. The optimal architecture obtains from maximizing the minimum number of linear regions approximated by a deep neural network with a ReLu activation function. The accuracy of the approximation function relies on the neural...