  Twin neural network regression (TNNR) is a semi-supervised regression
algorithm, it can be trained on unlabelled data points as long as other,
labelled anchor data points, are present. TNNR is trained to predict
differences between the target values of two different data points rather than
the targets themselves. By ensembling predicted differences between the targets
of an unseen data point and all training data points, it is possible to obtain
a very accurate prediction for the original regression problem. Since any loop
of predicted differences should sum to zero, loops can be supplied to the
training data, even if the data points themselves within loops are unlabelled.
Semi-supervised training improves TNNR performance, which is already state of
the art, significantly.
