39 citations · 51 across the 6 of their papers we have counts for
6 papers
From a Point Cloud to a Simulation Model: Bayesian Segmentation and Entropy based Uncertainty Estimation for 3D Modelling
Christina Petschnigg, Markus Spitzner, Lucas Weitzendorf +1
The 3D modelling of indoor environments and the generation of process simulations play an important role in factory and assembly planning. In brownfield planning cases existing dat…
A Bayesian Approach for Predicting Food and Beverage Sales in Staff Canteens and Restaurants
Konstantin Posch, Christian Truden, Philipp Hungerländer +1
Accurate demand forecasting is one of the key aspects for successfully managing restaurants and staff canteens. In particular, properly predicting future sales of menu items allows…
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…
A statistical noise model for a class of Physically Unclonable Functions
Benjamin Hackl, Daniel Kurz, Clemens Heuberger +2
The interest in "Physically Unclonable Function"-devices has increased rapidly over the last few years, as they have several interesting properties for system security related appl…