| Hyperspectral fruit and vegetable classification using convolutional neural networks J Steinbrener, K Posch, R Leitner Computers and Electronics in Agriculture 162, 364-372, 2019 | 142 | 2019 |
| Correlated parameters to accurately measure uncertainty in deep neural networks K Posch, J Pilz IEEE Transactions on Neural Networks and Learning Systems 32 (3), 1037-1051, 2020 | 34 | 2020 |
| Variational inference to measure model uncertainty in deep neural networks K Posch, J Steinbrener, J Pilz arXiv preprint arXiv:1902.10189, 2019 | 33 | 2019 |
| A Bayesian approach for predicting food and beverage sales in staff canteens and restaurants K Posch, C Truden, P Hungerländer, J Pilz International Journal of Forecasting 38 (1), 321-338, 2022 | 29 | 2022 |
| Measuring the uncertainty of predictions in deep neural networks with variational inference J Steinbrener, K Posch, J Pilz Sensors 20 (21), 6011, 2020 | 16 | 2020 |
| A novel Bayesian approach for variable selection in linear regression models K Posch, M Arbeiter, J Pilz Computational statistics & data analysis 144, 106881, 2020 | 9 | 2020 |
| Variable selection using nearest neighbor gaussian processes K Posch, M Arbeiter, M Pleschberger, J Pilz arXiv preprint arXiv:2103.14315, 2021 | 2 | 2021 |
| Regularization of Statistical Models with a Focus on Bayesian Methods K Posch Technische Universität Graz, 2020 | | 2020 |
