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Measuring the Uncertainty of Predictions in Deep Neural Networks with Variational Inference

We present a novel approach for training deep neural networks in a Bayesian way. Compared to other Bayesian deep learning formulations, our approach allows for quantifying the uncertainty in model parameters while only adding very few additional parameters to be optimized. The proposed approach uses...

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Bibliografski detalji
Izdano u:Sensors (Basel)
Glavni autori: Steinbrener, Jan, Posch, Konstantin, Pilz, Jürgen
Format: Artigo
Jezik:Inglês
Izdano: MDPI 2020
Teme:
Online pristup:https://ncbi.nlm.nih.gov/pmc/articles/PMC7660222/
https://ncbi.nlm.nih.gov/pubmed/33113927
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s20216011
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