Discovering Binary Codes for Documents by Learning Deep Generative Models

@article{Hinton2011DiscoveringBC,
  title={Discovering Binary Codes for Documents by Learning Deep Generative Models},
  author={Geoffrey E. Hinton and Ruslan Salakhutdinov},
  journal={Topics in cognitive science},
  year={2011},
  volume={3 1},
  pages={74-91}
}
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… CONTINUE READING
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