  Here we introduce the RobERt (Robotic Exoplanet Recognition) algorithm for
the classification of exoplanetary emission spectra. Spectral retrievals of
exoplanetary atmospheres frequently requires the preselection of
molecular/atomic opacities to be defined by the user. In the era of
open-source, automated and self-sufficient retrieval algorithms, manual input
should be avoided. User dependent input could, in worst case scenarios, lead to
incomplete models and biases in the retrieval. The RobERt algorithm is based on
deep belief neural (DBN) networks trained to accurately recognise molecular
signatures for a wide range of planets, atmospheric thermal profiles and
compositions. Reconstructions of the learned features, also referred to as
`dreams' of the network, indicate good convergence and an accurate
representation of molecular features in the DBN. Using these deep neural
networks, we work towards retrieval algorithms that themselves understand the
nature of the observed spectra, are able to learn from current and past data
and make sensible qualitative preselections of atmospheric opacities to be used
for the quantitative stage of the retrieval process.
