  Extensive research on formal verification of machine learning systems
indicates that learning from data alone often fails to capture underlying
background knowledge such as specifications implicitly available in the data.
Various neural network verifiers have been developed to ensure that a
machine-learnt model satisfies correctness and safety properties, however, they
typically assume a trained network with fixed weights. A promising approach for
creating machine learning models that inherently satisfy constraints after
training is to encode background knowledge as explicit logical constraints that
guide the learning process via so-called differentiable logics. In this paper,
we experimentally compare and evaluate various logics from the literature,
presenting our findings and highlighting open problems for future work.
