Discovering Binary Codes for Documents by Learning Deep Generative Models
topiCS, Volume 3, Issue 1, 2011, Pages 74-91.
Deep learningSemantic hashingAuto-encodersRestricted Boltzmann machinesDocument retrievalMore(1+)
We describe a deep generative model in which the lowest layer represents the word-count vector of a document and the top layer represents a learned binary code for that document. The top two layers of the generative model form an undirected associative memory and the remaining layers form a belief net with directed, top-down connections. ...More
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