Discovering binary codes for documents by learning deep generative modelsWOSEI

摘要

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. We present efficient learning and inference procedures for this type of generative model and show that it allows more accurate and much faster retrieval than latent semantic analysis. By using our met...更多
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topiCS, Volume 3, Issue 1, 2011, Pages 74-91.

被引用次数88|引用|7
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