Fetching the paper…
Reading the bibliography…
The neural linear model is a simple adaptive Bayesian linear regression method that has recently been used in a number of problems ranging from Bayesian optimization to reinforcement learning.
‘In-between’ uncertainty in Bayesian neural networks
Andrew YK Foong, Yingzhen Li, José Miguel Hernández-Lobato, and Richard E Turner · 1906
Earlier work this paper cites.
Pathologies of factorised Gaussian and MC dropout posteriors in Bayesian neural networks
Andrew YK Foong, David R Burt, Yingzhen Li, and Richard E Turner · 1909
Earlier work this paper cites.
Individual comparisons by ranking methods
Frank Wilcoxon · 1992
Earlier work this paper cites.
Keeping neural networks simple by minimizing the description length of the weights
Geoffrey Hinton and Drew Van Camp · 1993
Earlier work this paper cites.
Bayesian learning for neural networks
Radford M Neal · 1995
Earlier work this paper cites.
Slice sampling
Radford M Neal et al · 2003
Earlier work this paper cites.
Statistical comparisons of classifiers over multiple data sets
Janez Demšar · 2006
Earlier work this paper cites.
Gaussian Processes for Machine Learning
Carl Edward Rasmussen and Christopher KI Williams · 2006
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
Cited alongside, same era.
Practical Bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
Cited alongside, same era.
ADAM: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Cited alongside, same era.
Probabilistic backpropagation for scalable learning of Bayesian neural networks
José Miguel Hernández-Lobato and Ryan Adams · 2015
Cited alongside, same era.
Variational dropout and the local reparameterization trick
Deep Gaussian processes for regression using approximate expectation propagation
Thang Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, and Richard Turner · 2016
Later among the works it cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Later among the works it cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Later among the works it cites.
Carlos Riquelme, George Tucker, and Jasper Snoek · 2018
Later among the works it cites.
Bayesian batch active learning as sparse subset approximation
Robert Pinsler, Jonathan Gordon, Eric Nalisnick, and José Miguel Hernández-Lobato · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Durk P Kingma, Tim Salimans, and Max Welling · 2015
Cited alongside, same era.
Scalable Bayesian optimization using deep neural networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, and Ryan Adams · 2015
Cited alongside, same era.
Neural network ensembles and variational inference revisited
Marcin B Tomczak, Siddharth Swaroop, and Richard E Turner
Cited in the paper.
Quality of uncertainty quantification for Bayesian neural network inference
Jiayu Yao, Weiwei Pan, Soumya Ghosh, and Finale Doshi-Velez · 2019
Closest in time.
Adaptive Bayesian linear regression for automated machine learning
Weilin Zhou and Frederic Precioso · 2019
Closest in time.