Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility

17 May 2022

Papers citing "Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility"

9 / 9 papers shown

Title
Deep Kernel Posterior Learning under Infinite Variance Prior Weights Jorge Loría A. Bhadra BDL UQCV 61 0 0 02 Oct 2024
Wide stable neural networks: Sample regularity, functional convergence and Bayesian inverse problems Tomás Soto 32 0 0 04 Jul 2024
Gaussian random field approximation via Stein's method with applications to wide random neural networks Krishnakumar Balasubramanian L. Goldstein Nathan Ross Adil Salim 30 8 0 28 Jun 2023
Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD Yijun Wan Melih Barsbey A. Zaidi Umut Simsekli 30 1 0 13 Jun 2023
Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance Jorge Loría A. Bhadra UQCV BDL 26 1 0 18 May 2023
Infinitely wide limits for deep Stable neural networks: sub-linear, linear and super-linear activation functions Alberto Bordino Stefano Favaro S. Fortini 30 7 0 08 Apr 2023
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning François Caron Fadhel Ayed Paul Jung Hoileong Lee Juho Lee Hongseok Yang 62 2 0 02 Feb 2023
Large-width asymptotics for ReLU neural networks with $α$ -Stable initializations Stefano Favaro S. Fortini Stefano Peluchetti 20 2 0 16 Jun 2022
Why bigger is not always better: on finite and infinite neural networks Laurence Aitchison 175 51 0 17 Oct 2019