Improving Neural Additive Models with Bayesian Principles

26 May 2023

Papers citing "Improving Neural Additive Models with Bayesian Principles"

6 / 6 papers shown

Title
Learning Layer-wise Equivariances Automatically using Gradients Tycho F. A. van der Ouderaa Alexander Immer Mark van der Wilk MLT 31 12 0 09 Oct 2023
Probing as Quantifying Inductive Bias Alexander Immer Lucas Torroba Hennigen Vincent Fortuin Ryan Cotterell 29 14 0 15 Oct 2021
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam Mohammad Emtiyaz Khan Didrik Nielsen Voot Tangkaratt Wu Lin Y. Gal Akash Srivastava ODL 74 264 0 13 Jun 2018
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,635 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,042 0 06 Jun 2015
MCMC using Hamiltonian dynamics Radford M. Neal 130 3,260 0 09 Jun 2012