  In supervised machine learning, the choice of loss function implicitly
assumes a particular noise distribution over the data. For example, the
frequently used mean squared error (MSE) loss assumes a Gaussian noise
distribution. The choice of loss function during training and testing affects
the performance of artificial neural networks (ANNs). It is known that MSE may
yield substandard performance in the presence of outliers. The Cauchy loss
function (CLF) assumes a Cauchy noise distribution, and is therefore
potentially better suited for data with outliers. This papers aims to determine
the extent of robustness and generalisability of the CLF as compared to MSE.
CLF and MSE are assessed on a few handcrafted regression problems, and a
real-world regression problem with artificially simulated outliers, in the
context of ANN training. CLF yielded results that were either comparable to or
better than the results yielded by MSE, with a few notable exceptions.
