Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Duerr, Oliver"'
Semi-structured regression models enable the joint modeling of interpretable structured and complex unstructured feature effects. The structured model part is inspired by statistical models and can be used to infer the input-output relationship for f
Externí odkaz:
http://arxiv.org/abs/2401.12950
Deep neural networks (NNs) are known for their high-prediction performances. However, NNs are prone to yield unreliable predictions when encountering completely new situations without indicating their uncertainty. Bayesian variants of NNs (BNNs), suc
Externí odkaz:
http://arxiv.org/abs/2308.12785
This study aims to investigate the utilization of Bayesian techniques for the calibration of micro-electro-mechanical systems (MEMS) accelerometers. These devices have garnered substantial interest in various practical applications and typically requ
Externí odkaz:
http://arxiv.org/abs/2306.06144
Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classical regression models either break down under the computational load o
Externí odkaz:
http://arxiv.org/abs/2211.13665
Autor:
Herzog, Lisa, Kook, Lucas, Götschi, Andrea, Petermann, Katrin, Hänsel, Martin, Hamann, Janne, Dürr, Oliver, Wegener, Susanne, Sick, Beate
In many medical applications, interpretable models with high prediction performance are sought. Often, those models are required to handle semi-structured data like tabular and image data. We show how to apply deep transformation models (DTMs) for di
Externí odkaz:
http://arxiv.org/abs/2206.13302
The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high fluctuations and increasing electrification cause huge forecast variabili
Externí odkaz:
http://arxiv.org/abs/2204.13939
Variational inference (VI) is a technique to approximate difficult to compute posteriors by optimization. In contrast to MCMC, VI scales to many observations. In the case of complex posteriors, however, state-of-the-art VI approaches often yield unsa
Externí odkaz:
http://arxiv.org/abs/2202.05650
The main challenge in Bayesian models is to determine the posterior for the model parameters. Already, in models with only one or few parameters, the analytical posterior can only be determined in special settings. In Bayesian neural networks, variat
Externí odkaz:
http://arxiv.org/abs/2106.00528
Outcomes with a natural order commonly occur in prediction tasks and often the available input data are a mixture of complex data like images and tabular predictors. Deep Learning (DL) models are state-of-the-art for image classification tasks but fr
Externí odkaz:
http://arxiv.org/abs/2010.08376
Publikováno v:
Medical Image Analysis (2020): 101790
At present, the majority of the proposed Deep Learning (DL) methods provide point predictions without quantifying the models uncertainty. However, a quantification of the reliability of automated image analysis is essential, in particular in medicine
Externí odkaz:
http://arxiv.org/abs/2008.06332