18 citations · 20 across the 5 of their papers we have counts for
4 papers · 1 filter
Deep Learning for Climate Model Output Statistics
Michael Steininger, Daniel Abel, Katrin Ziegler +3
Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitatio…
Anomaly Detection in Beehives using Deep Recurrent Autoencoders
Padraig Davidson, Michael Steininger, Florian Lautenschlager +3
Precision beekeeping allows to monitor bees' living conditions by equipping beehives with sensors. The data recorded by these hives can be analyzed by machine learning models to le…
SimLoss: Class Similarities in Cross Entropy
Konstantin Kobs, Michael Steininger, Albin Zehe +2
One common loss function in neural network classification tasks is Categorical Cross Entropy (CCE), which punishes all misclassifications equally. However, classes often have an in…
MapLUR: Exploring a new Paradigm for Estimating Air Pollution using Deep Learning on Map Images
Michael Steininger, Konstantin Kobs, Albin Zehe +3
Land-use regression (LUR) models are important for the assessment of air pollution concentrations in areas without measurement stations. While many such models exist, they often us…