Sep 21, 2020 · First, we compute the NTK for a considered ResNet model and prove its stability during gradient descent training. Then, we show by various ...

Abstract. A major factor in the success of deep neural networks is the use of sophisticated architectures rather than the classical multilayer perceptron ...

Kernel-Based Smoothness Analysis of Residual Networks - tomtirer/ResNet-NTK.

We analyze this phenomenon via the neural tangent kernel (NTK) approach. First, we compute the NTK for a considered ResNet model and prove its stability during ...

This paper shows another distinction between the two models, namely, a tendency of ResNets to promote smoother interpolations than MLPs, via the neural ...

Previous works focused on the optimization advantages of deep ResNets over deep MLPs. In this paper, we show another distinction between the two models, namely, ...

Kernel-Based Smoothness Analysis of Residual Networks. Tom Tirer 1. Joan Bruna ... Decreasing α yields a smoother ResNet NTK with smoother interpolation results.

Our smoothness findings are based on different evaluation methodologies, such as visualizing the kernel of each model (which is data- independent), comparing ...

Kernel-Based Smoothness Analysis of Residual Networks. MSML 2021: 921-954 ... Kernel-Based Smoothness Analysis of Residual Networks. CoRR abs ...

Kernel-Based Smoothness Analysis of Residual Networks, Tirer T, Bruna J, Giryes, R., MSML21. Neural Splines: Fitting 3d surfaces with Ininitely-Wide Neural ...