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08/24/2023
Single-shot Bayesian approximation for neural networks
Deep neural networks (NNs) are known for their high-prediction performan...
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06/09/2023
Bayesian Calibration of MEMS Accelerometers
This study aims to investigate the utilization of Bayesian techniques fo...
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11/24/2022
Estimating Conditional Distributions with Neural Networks using R package deeptrafo
Contemporary empirical applications frequently require flexible regressi...
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06/27/2022
Deep transformation models for functional outcome prediction after acute ischemic stroke
For many medical applications, interpretable models with a high predicti...
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04/29/2022
Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows
The transition to a fully renewable energy grid requires better forecast...
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02/11/2022
Bernstein Flows for Flexible Posteriors in Variational Bayes
Variational inference (VI) is a technique to approximate difficult to co...
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06/01/2021
Transformation Models for Flexible Posteriors in Variational Bayes
The main challenge in Bayesian models is to determine the posterior for ...
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10/16/2020
Ordinal Neural Network Transformation Models: Deep and interpretable regression models for ordinal outcomes
Outcomes with a natural order commonly occur in prediction tasks and oft...
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08/13/2020
Integrating uncertainty in deep neural networks for MRI based stroke analysis
At present, the majority of the proposed Deep Learning (DL) methods prov...
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07/07/2020
Single Shot MC Dropout Approximation
Deep neural networks (DNNs) are known for their high prediction performa...
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04/01/2020
Deep transformation models: Tackling complex regression problems with neural network based transformation models
We present a deep transformation model for probabilistic regression. Dee...
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07/11/2018
