23 citations · 32 across the 7 of their papers we have counts for
4 papers · 1 filter
Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study
Franck Gabriel, François Ged, Maria Han Veiga +1
Diffusion models now set the benchmark in high-fidelity generative sampling, yet they can, in principle, be prone to memorization. In this case, their learned score overfits the fi…
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…
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Arthur Jacot, Franck Gabriel, Clément Hongler
At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit, thus connecting them to kernel methods. We prove that the evo…