A Fast Learning Algorithm for Deep Belief Nets
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Abstract
We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind.
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The record
- Venue
- Neural Computation
- Topic
- Domain Adaptation and Few-Shot Learning
- Field
- Computer Science
- Canadian institutions
- University of Toronto
- Funders
- Natural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanadian Institute for Advanced Research
- Keywords
- Computer scienceAssociative propertyPrior probabilityDiscriminative modelGenerative modelContent-addressable memoryArtificial intelligenceInferenceAlgorithmGenerative grammarPattern recognition (psychology)Deep belief networkArtificial neural networkMathematicsBayesian probability
- Has abstract in OpenAlex
- yes