Zobrazeno 1 - 10
of 221
pro vyhledávání: '"PILZ, Jürgen"'
Autor:
Sommeregger, Lukas a, ⁎, Pilz, Jürgen b
Publikováno v:
In Microelectronics Reliability January 2025 164
Publikováno v:
Machine Learning (2022)
Feature selection represents a measure to reduce the complexity of high-dimensional datasets and gain insights into the systematic variation in the data. This aspect is of specific importance in domains that rely on model interpretability, such as li
Externí odkaz:
http://arxiv.org/abs/2104.14787
Autor:
Posch, Konstantin, Arbeiter, Maximilian, Truden, Christian, Pleschberger, Martin, Pilz, Juergen
We introduce a novel Bayesian approach for variable selection using Gaussian process regression, which is crucial for enhancing interpretability and model regularization. Our method employs nearest neighbor Gaussian processes, serving as scalable app
Externí odkaz:
http://arxiv.org/abs/2103.14315
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 In brownfield planning cases existing data are often outdated and incomplete especially for older plants, which
Externí odkaz:
http://arxiv.org/abs/2102.02488
Autor:
Petschnigg, Christina, Pilz, Juergen
The digital factory provides undoubtedly a great potential for future production systems in terms of efficiency and effectivity. A key aspect on the way to realize the digital copy of a real factory is the understanding of complex indoor environments
Externí odkaz:
http://arxiv.org/abs/2012.07038
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 a precise ordering of food stock. From an environmental point of view
Externí odkaz:
http://arxiv.org/abs/2005.12647
Autor:
Posch, Konstantin, Pilz, Jürgen
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 uncertainty and additionally is robust to overfitting. These are commonly the t
Externí odkaz:
http://arxiv.org/abs/1904.01334
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 direct specification of a-priori beliefs about the number of nonzero reg
Externí odkaz:
http://arxiv.org/abs/1903.05367