Matches in SemOpenAlex for { <https://semopenalex.org/work/W60493759> ?p ?o ?g. }
- W60493759 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, auto-encoders, 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." @default.
- W60493759 created "2016-06-24" @default.
- W60493759 creator A5031185465 @default.
- W60493759 creator A5050254224 @default.
- W60493759 creator A5086198262 @default.
- W60493759 date "2012-06-24" @default.
- W60493759 modified "2023-10-06" @default.
- W60493759 title "Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives" @default.
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