# A Backward SDE Method for Uncertainty Quantification in Deep Learning

arXiv: https://arxiv.org/abs/2011.14145 · HTML: https://mlsanity.com/paper/2011.14145/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Federated Learning on Stochastic Neural Networks](https://mlsanity.com/paper/2506.08169/) (2025)

## Abstract (arXiv)

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.