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- W2731105127 abstract "Learning by children and animals occurs effortlessly and largely without obvious supervision. Successes in automating supervised learning have not translated to the more ambiguous realm of unsupervised learning where goals and labels are not provided. Barlow (1961) suggested that the signal that brains leverage for unsupervised learning is dependence, or redundancy, in the sensory environment. Dependence can be characterized using the information-theoretic multivariate mutual information measure called total correlation. The principle of Total Cor-relation Ex-planation (CorEx) is to learn representations of data that explain as much dependence in the data as possible. We review some manifestations of this principle along with successes in unsupervised learning problems across diverse domains including human behavior, biology, and language." @default.
- W2731105127 created "2017-07-14" @default.
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- W2731105127 date "2017-06-27" @default.
- W2731105127 modified "2023-09-25" @default.
- W2731105127 title "Unsupervised Learning via Total Correlation Explanation" @default.
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