Neural tangent kernel: convergence and generalization in neural networks (invited paper)
Arthur Paul Jacot, Franck Gabriel, Clément Hongler · 2021
The Neural Tangent Kernel is a new way to understand the gradient descent in deep neural networks, connecting them with kernel methods. In this talk, I'll introduce this formalism and give a number of results on the Neural Tangent Kernel and explain how they give us insight into the dynamics of neural networks during training and into their generalization features.