Variance Reduction in Stochastic Gradient Langevin DynamicsEIWOS

    Cited by: 37|Bibtex|32|

    NIPS, pp. 1154-1162, 2016.

    Abstract:

    Stochastic gradient-based Monte Carlo methods such as stochastic gradient Langevin dynamics are useful tools for posterior inference on large scale datasets in many machine learning applications. These methods scale to large datasets by using noisy gradients calculated using a mini-batch or subset of the dataset. However, the high varianc...More
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