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Representation Learning: A Review and New Perspectives
Aug. 2013 (vol. 35 no. 8)
pp. 1798-1828
Yoshua Bengio, University of Montreal, Montreal
Aaron Courville, University of Montreal, Montreal
Pascal Vincent, University of Montreal, Montreal
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.
Index Terms:
Learning systems,Machine learning,Abstracts,Feature extraction,Manifolds,Neural networks,Speech recognition,neural nets,Deep learning,representation learning,feature learning,unsupervised learning,Boltzmann machine,autoencoder
Yoshua Bengio, Aaron Courville, Pascal Vincent, "Representation Learning: A Review and New Perspectives," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798-1828, Aug. 2013, doi:10.1109/TPAMI.2013.50
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