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stat.ML2021
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…
stat.ML2019★ 12 cited
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…