  Reliably characterised pulses are the starting point of any application of
ultrafast techniques. Unfortunately, experimental constraints do not always
allow optimising the characterisation conditions. This dictates the need for
refined analysis methods. Here we show that neutral networks can provide a
viable characterisation when applied to data from SPIDER. We have adopted a
cascade of convolutional networks, addressing the multiparameter structure of
the interferogram with a reasonable computing power. In particular, the
necessity of precalibration is reduced, thus pointing towards the introduction
of neural networks in more generic arrangements.
