A Backward SDE Method for Uncertainty Quantification in Deep Learning

• URL: http://arxiv.org/abs/2011.14145v2
• Date: Sun, 4 Apr 2021 01:42:45 GMT
• Title: A Backward SDE Method for Uncertainty Quantification in Deep Learning
• Authors: Richard Archibald, Feng Bao, Yanzhao Cao, and He Zhang
• Abstract summary: We develop a probabilistic machine learning method, which formulates a class of neural networks by an optimal control problem. An efficient descent algorithm is introduced under the maximum principle framework.
• Score: 9.7140720884508
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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