Bayesian learning via stochastic gradient langevin dynamics
In this paper we propose a new framework for learning from large scale datasets based on iterative learning from small mini-batches. By adding the right amount of noise to a standard stochastic gradient optimization algorithm we show that the iterates will converge to samples from the true posterior...
| Main Authors: | , |
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| Format: | Journal article |
| Language: | English |
| Published: |
2011
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