title: Representation learning: a review and new perspectives.
abstract: The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.
authors.affiliation: Department of Computer Science and Operations Research, University of Montreal, Montreal, Quebec H3C 3J7, Canada.
authors.forename: Yoshua
authors.initials: Y
authors.lastname: Bengio
authors.forename: Aaron
authors.initials: A
authors.lastname: Courville
authors.forename: Pascal
authors.initials: P
authors.lastname: Vincent
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence / Q000639:trends*; D006801:Humans; D016571:Neural Networks, Computer