no code implementations • 8 May 2024 • Lucas Kook, Chris Kolb, Philipp Schiele, Daniel Dold, Marcel Arpogaus, Cornelius Fritz, Philipp F. Baumann, Philipp Kopper, Tobias Pielok, Emilio Dorigatti, David Rügamer
Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithms.
no code implementations • 20 Feb 2024 • Cornelius Fritz, Co-Pierre Georg, Angelo Mele, Michael Schweinberger
Modern software development involves collaborative efforts and reuse of existing code, which reduces the cost of developing new software.
2 code implementations • 6 Apr 2021 • David Rügamer, Chris Kolb, Cornelius Fritz, Florian Pfisterer, Philipp Kopper, Bernd Bischl, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, Philipp Baumann, Lucas Kook, Nadja Klein, Christian L. Müller
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks.
no code implementations • 3 Jan 2021 • Cornelius Fritz, Emilio Dorigatti, David Rügamer
The results corroborate the necessity of including mobility data and showcase the flexibility and interpretability of our approach.
no code implementations • 15 Dec 2020 • Cornelius Fritz, Marius Mehrl, Paul W. Thurner, Göran Kauermann
Accurate and interpretable forecasting models predicting spatially and temporally fine-grained changes in the numbers of intrastate conflict casualties are of crucial importance for policymakers and international non-governmental organisations (NGOs).
Applications