39 citations · 52 across the 5 of their papers we have counts for
3 papers · 1 filter
Correlated Parameters to Accurately Measure Uncertainty in Deep Neural Networks
Konstantin Posch, Jürgen Pilz
In this article a novel approach for training deep neural networks using Bayesian techniques is presented. The Bayesian methodology allows for an easy evaluation of model uncertain…
A novel Bayesian approach for variable selection in linear regression models
Konstantin Posch, Maximilian Arbeiter, Jürgen Pilz
We propose a novel Bayesian approach to the problem of variable selection in multiple linear regression models. In particular, we present a hierarchical setting which allows for di…
Variational Inference to Measure Model Uncertainty in Deep Neural Networks
Konstantin Posch, Jan Steinbrener, Jürgen Pilz
We present a novel approach for training deep neural networks in a Bayesian way. Classical, i.e. non-Bayesian, deep learning has two major drawbacks both originating from the fact…