A theory of representation learning gives a deep generalisation of kernel methods

30 August 2021

Papers citing "A theory of representation learning gives a deep generalisation of kernel methods"

7 / 7 papers shown

Title
Estimating the Spectral Moments of the Kernel Integral Operator from Finite Sample Matrices Chanwoo Chun SueYeon Chung Daniel D. Lee 26 1 0 23 Oct 2024
Deep Kernel Posterior Learning under Infinite Variance Prior Weights Jorge Loría A. Bhadra BDL UQCV 61 0 0 02 Oct 2024
Function-Space MCMC for Bayesian Wide Neural Networks Lucia Pezzetti Stefano Favaro Stefano Peluchetti BDL 127 0 0 26 Aug 2024
Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks Blake Bordelon C. Pehlevan MLT 38 29 0 06 Apr 2023
Deep Layer-wise Networks Have Closed-Form Weights Chieh-Tsai Wu A. Masoomi A. Gretton Jennifer Dy 29 3 0 01 Feb 2022
Why bigger is not always better: on finite and infinite neural networks Laurence Aitchison 175 51 0 17 Oct 2019
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015