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\subsection{Autoencoders}  In 2007, Yoshua Bengio, professor at the University of Montreal, presented an alternative to RBMs: autoencoders\cite{NI  S2006_3048}. PS2006_3048}.  Autoencoders are neural networks that learn to compress and process their input data. After the procesing that they do, the most relevant features of the input data are extracted and they can be used to solve our machine learning problem, such as recognizing objects in images, more easily.