Publication

Neural Tangent Kernel: Convergence and Generalization in Neural Networks (Invited Paper)

Clément Hongler, Arthur Ulysse Jacot-Guillarmod
2021
Conference Papers

Abstract

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.

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Neural Tangent Kernel: Convergence and Generalization in Neural Networks (Invited Paper)

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