Verifying properties and interpreting the behaviour of deep neural networks
(DNN) is an important task given their ubiquitous use in applications,
including safety-critical ones, and their blackbox nature. We propose an
automata-theoric approach to tackling problems arising in DNN analysis. We show
that the input-output behaviour of a DNN can be captured precisely by a
(special) weak B\"uchi automaton of exponential size. We show how these can be
used to address common verification and interpretation tasks like adversarial
robustness, minimum sufficient reasons etc. We report on a proof-of-concept
implementation translating DNN to automata on finite words for better
efficiency at the cost of losing precision in analysis