  Finite linear temporal logic ($\mathsf{LTL}_f$) is a powerful formal
representation for modeling temporal sequences. We address the problem of
learning a compact $\mathsf{LTL}_f$ formula from labeled traces of system
behavior. We propose a novel neural network operator and evaluate the resulting
architecture, Neural$\mathsf{LTL}_f$. Our approach includes a specialized
recurrent filter, designed to subsume $\mathsf{LTL}_f$ temporal operators, to
learn a highly accurate classifier for traces. Then, it discretizes the
activations and extracts the truth table represented by the learned weights.
This truth table is converted to symbolic form and returned as the learned
formula. Experiments on randomly generated $\mathsf{LTL}_f$ formulas show
Neural$\mathsf{LTL}_f$ scales to larger formula sizes than existing approaches
and maintains high accuracy even in the presence of noise.
