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Abstract
We present a new learning algorithm for Boltz-mann machines that contain many layers of hid-den variables. Data-dependent expectations are estimated using a variational approximation that tends to focus on a single mode, and data-independent expectations are approximated us-ing persistent Markov chains. The use of two quite different techniques for estimating the two types of expectation that enter into the gradient of the log-likelihood makes it practical to learn Boltzmann machines with multiple hidden lay-ers and millions of parameters. The learning can be made more efficient by using a layer-by-layer “pre-training ” phase that allows variational in-ference to be initialized with a single bottom-up pass. We present results on the MNIST and NORB datasets showing that deep Boltzmann machines learn good generative models and per-form well on handwritten digit and visual object recognition tasks. 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Topic
- Generative Adversarial Networks and Image Synthesis
- Field
- Computer Science
- Canadian institutions
- University of Toronto
- Funders
- —
- Keywords
- Boltzmann machineMNIST databaseRestricted Boltzmann machineFocus (optics)Computer scienceInferenceArtificial intelligenceBoltzmann constantHidden Markov modelHidden variable theoryMarkov chainObject (grammar)Generative modelDeep learningBoltzmann distributionLayer (electronics)AlgorithmPattern recognition (psychology)Machine learningGenerative grammarStatistical physicsPhysics
- Has abstract in OpenAlex
- yes