Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations

Understanding the training dynamics of deep learning models is perhaps a necessary step toward demystifying the effectiveness of these models. In particular, how do data from different classes gradually become separable in their feature spaces when training neural networks using stochastic gradient descent?