  Neural Network (NN) classifiers can assign extreme probabilities to samples
that have not appeared during training (out-of-distribution samples) resulting
in erroneous and unreliable predictions. One of the causes for this unwanted
behaviour lies in the use of the standard softmax operator which pushes the
posterior probabilities to be either zero or unity hence failing to model
uncertainty. The statistical derivation of the softmax operator relies on the
assumption that the distributions of the latent variables for a given class are
Gaussian with known variance. However, it is possible to use different
assumptions in the same derivation and attain from other families of
distributions as well. This allows derivation of novel operators with more
favourable properties. Here, a novel operator is proposed that is derived using
$t$-distributions which are capable of providing a better description of
uncertainty. It is shown that classifiers that adopt this novel operator can be
more robust to out of distribution samples, often outperforming NNs that use
the standard softmax operator. These enhancements can be reached with minimal
changes to the NN architecture.
