title: The asymptotic spectrum of the Hessian of DNN throughout training
abstract: The dynamics of DNNs during gradient descent is described by the so-called Neural Tangent Kernel (NTK). In this article, we show that the NTK allows one to gain precise insight into the Hessian of the cost of DNNs: we obtain a full characterization of the asymptotics of the spectrum of the Hessian, at initialization and during training. 
authors: Arthur Jacot Franck Gabriel Clement Hongler
hIndex: Arthur Jacot 10