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.
翻译:我们开发了一种概率机器学习方法,该方法通过一种随机最佳控制问题来形成一组随机神经网络。在随机最大原则框架下引入了高效随机梯度梯度下降算法。 应用随机神经网络的数值实验正在进行,以验证我们的方法的有效性。