Building on an economic model of rational Bitcoin mining, we measured the carbon footprint of Bitcoin mining power consumption using feed-forward neural networks. We found associated carbon footprints of 2.77, 16.08 and 14.99 MtCO2e for 2017, 2018 and 2019 based on a novel bottom-up approach,...

This paper presents an overview of the procedures that are involved in prediction with machine learning models with special emphasis on deep learning. We study suitable objective functions for prediction in high-dimensional settings and discuss the role of regularization methods in order to...

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...

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...