Deep Learning of Representations

Unsupervised learning of representations has been found useful in many applications and benefits from several advantages, e.g., where there are many unlabeled examples and few labeled ones (semi-supervised learning), or where the unlabeled or labeled examples are from a distribution different but related to the one of interest (self-taught learning, multi-task learning, and domain adaptation). Some of these algorithms have successfully been used to learn a hierarchy of features, i.e., to build a deep architecture, either as initialization for a supervised predictor, or as a generative model. Deep learning algorithms can yield representations that are more abstract and better disentangle the hidden factors of variation underlying the unknown generating distribution, i.e., to capture invariances and discover non-local structure in that distribution. This chapter reviews the main motivations and ideas behind deep learning algorithms and their representation-learning components, as well as recent results in this area, and proposes a vision of challenges and hopes on the road ahead, focusing on the questions of invariance and disentangling.