Monotonicity constraints are powerful regularizers in statistical modelling.
They can support fairness in computer supported decision making and increase
plausibility in data-driven scientific models. The seminal min-max (MM) neural
network architecture ensures monotonicity, but often gets stuck in undesired
local optima during training because of vanishing gradients. We propose a
simple modification of the MM network using strictly-increasing smooth
non-linearities that alleviates this problem. The resulting smooth min-max
(SMM) network module inherits the asymptotic approximation properties from the
MM architecture. It can be used within larger deep learning systems trained
end-to-end. The SMM module is considerably simpler and less computationally
demanding than state-of-the-art neural networks for monotonic modelling. Still,
in our experiments, it compared favorably to alternative neural and non-neural
approaches in terms of generalization performance