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A Backward SDE Method for Uncertainty Quantification in Deep Learning

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arxiv 2011.14145 v2 pith:2NSCPTOF submitted 2020-11-28 cs.LG math.OCstat.ML

Richard Archibald , Feng Bao , Yanzhao Cao , He Zhang This is my paper · ORCID
classification cs.LGmath.OCstat.ML
keywords stochasticlearningmethodnetworksneuralalgorithmapplicationsbackward
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We develop a probabilistic machine learning method, which formulates a class of stochastic neural networks by a stochastic optimal control problem. An efficient stochastic gradient descent algorithm is introduced under the stochastic maximum principle framework. Numerical experiments for applications of stochastic neural networks are carried out to validate the effectiveness of our methodology.

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  1. Federated Learning on Stochastic Neural Networks

    cs.LG 2025-06 conditional novelty 5.0 of 10

    A federated learning algorithm that trains local stochastic neural networks to capture both the true function and the noise in each client's data.

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