Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization

1 February 2022

Papers citing "Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization"

6 / 6 papers shown

Title
The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks Andrea Bonfanti Giuseppe Bruno Cristina Cipriani 32 9 0 06 Feb 2024
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension Moritz Haas David Holzmüller U. V. Luxburg Ingo Steinwart MLT 42 14 0 23 May 2023
Efficient Parametric Approximations of Neural Network Function Space Distance Nikita Dhawan Sicong Huang Juhan Bae Roger C. Grosse 16 5 0 07 Feb 2023
Approximation results for Gradient Descent trained Shallow Neural Networks in $1d$ R. Gentile G. Welper ODL 66 6 0 17 Sep 2022
Deep Networks and the Multiple Manifold Problem Sam Buchanan D. Gilboa John N. Wright 166 39 0 25 Aug 2020
Why bigger is not always better: on finite and infinite neural networks Laurence Aitchison 175 51 0 17 Oct 2019