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The asymptotic spectrum of the Hessian of DNN throughout training
Arthur Jacot, Franck Gabriel, Clément Hongler
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 int…
Order and Chaos: NTK views on DNN Normalization, Checkerboard and Boundary Artifacts
Arthur Jacot, Franck Gabriel, François Ged +1
We analyze architectural features of Deep Neural Networks (DNNs) using the so-called Neural Tangent Kernel (NTK), which describes the training and generalization of DNNs in the inf…
Disentangling feature and lazy training in deep neural networks
Mario Geiger, Stefano Spigler, Arthur Jacot +1
Two distinct limits for deep learning have been derived as the network width , depending on how the weights of the last layer scale with . In the Neural Tan…
Scaling description of generalization with number of parameters in deep learning
Mario Geiger, Arthur Jacot, Stefano Spigler +6
Supervised deep learning involves the training of neural networks with a large number of parameters. For large enough , in the so-called over-parametrized regime, one can es…