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Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition Marc’Aurelio Ranzato, Fu Jie Huang, Y-Lan Boureau, Yann LeCun Courant Institute of Mathematical Sciences, New York Universi
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Document Date: 2009-08-08 18:43:08

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File Size: 337,22 KB

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Company

IEEE Press / MIT Press / Convolutional Networks / /

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Facility

Yann LeCun Courant Institute of Mathematical Sciences / Canadian Institute of Advanced Research / New York University / /

IndustryTerm

deep multilayer network / filter bank / supervised gradient-based algorithm / vision applications / natural solution / neural network / decoder energy / iterative algorithm / on-line learning algorithm / bank / energy-based model / unsupervised learning algorithm / dot-product / auto-encoder neural network / simplest baseline systems / above algorithm / encoder energy / invariant learning algorithm / experiment protocol / learning algorithm / energy / /

Organization

Yann LeCun Courant Institute of Mathematical Sciences / New York University / New York / National Science Foundation / MIT / US Federal Reserve / Univ. of Chicago / Canadian Institute of Advanced Research / /

Person

Sebastian Seung / Jie Huang / Geoffrey Hinton / Yoshua Bengio / Aurelio Ranzato / /

Position

feature extractor / sparse and shift-invariant feature extractor / resulting feature extractor / second level feature extractor / invariant feature extractor / shift-invariant unsupervised feature extractor / first stage extractor / first stage feature extractor / /

ProgrammingLanguage

EC / /

Technology

learning algorithm / above algorithm / experiment protocol / neural network / proposed algorithm / gradient-based algorithm / invariant learning algorithm / iterative algorithm / unsupervised learning algorithm / on-line learning algorithm / supervised gradient-based algorithm / same algorithm / same unsupervised learning algorithm / /

URL

http /

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