论文标题
深度学习中不确定性量化的向后SDE方法
A Backward SDE Method for Uncertainty Quantification in Deep Learning
论文作者
论文摘要
我们开发了一种概率的机器学习方法，该方法通过随机的最佳控制问题来制定一类随机神经网络。在随机最大原理框架下引入了有效的随机梯度下降算法。进行随机神经网络应用的数值实验以验证我们方法的有效性。
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.
