论文标题
没有梯度的训练 - 一种过滤方法
Training without Gradients -- A Filtering Approach
论文作者
论文摘要
建议在不利用梯度计算的情况下训练多层神经网络的粒子过滤方法。网络权重被认为是噪声驱动的线性系统估计状态向量的组成部分，而神经网络则是估计问题中的测量函数。一个简单的例子用于提供该概念的初步演示，该概念尚待进一步研究培训深层神经网络。
A particle filtering approach is suggested for the training of multi-layer neural networks without utilizing gradients calculation. The network weights are considered to be the components of the estimated state-vector of a noise driven linear system, whereas the neural network serves as the measurement function in the estimation problem. A simple example is used to provide a preliminary demonstration of the concept, which remains to be further studied for training deep neural networks.
