Quantitative Gaussian Approximation of Randomly Initialized Deep Neural Networks

14 March 2022

Papers citing "Quantitative Gaussian Approximation of Randomly Initialized Deep Neural Networks"

8 / 8 papers shown

Title
Fractal and Regular Geometry of Deep Neural Networks Simmaco Di Lillo Domenico Marinucci Michele Salvi Stefano Vigogna MDE AI4CE 36 0 0 08 Apr 2025
Student-t processes as infinite-width limits of posterior Bayesian neural networks Francesco Caporali Stefano Favaro Dario Trevisan BDL 197 0 0 06 Feb 2025
Spectral complexity of deep neural networks Simmaco Di Lillo Domenico Marinucci Michele Salvi Stefano Vigogna BDL 82 1 0 15 May 2024
Quantitative CLTs in Deep Neural Networks Stefano Favaro Boris Hanin Domenico Marinucci I. Nourdin G. Peccati BDL 33 12 0 12 Jul 2023
Non-asymptotic approximations of Gaussian neural networks via second-order Poincaré inequalities Alberto Bordino Stefano Favaro S. Fortini 20 7 0 08 Apr 2023
Large-width asymptotics for ReLU neural networks with $α$ -Stable initializations Stefano Favaro S. Fortini Stefano Peluchetti 20 2 0 16 Jun 2022
Gaussian Pre-Activations in Neural Networks: Myth or Reality? Pierre Wolinski Julyan Arbel AI4CE 73 8 0 24 May 2022
Non-asymptotic approximations of neural networks by Gaussian processes Ronen Eldan Dan Mikulincer T. Schramm 38 24 0 17 Feb 2021