B Sick, T Hathorn, O Dürr - 2021
Abstract: We present a deep transformation model for probabilistic regression. Deep learning is known for outstandingly accurate predictions on complex data but in regression tasks it is predominantly used to just predict a single number. This...
O Dürr, B Sick, E Murina - 2020
Probabilistic Deep Learning is a hands-on guide to the principles that support neural
networks. Learn to improve network performance with the right distribution for different data
types, and discover Bayesian variants that can state their own uncertainty to increase
accuracy. This...
M Arpogaus, M Voß, B Sick, M Nigge-Uricher, O Dürr - ICML 2021, Workshop Tackling Climate Change with Machine …, 2021
Abstract: The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level. However, high fluctuations and increasing electrification cause...
O Dürr, T Volz, W Dieterich, A Nitzan - The Journal of chemical physics, 2002
Abstract: Tracer-diffusion of small molecules through dense systems of chain polymers is studied
within an athermal lattice model, where hard-core interactions are taken into account by
means of the site exclusion principle. An...
O Dürr, W Dieterich, A Nitzan - The Journal of chemical physics, 2004
Abstract: Monte Carlo simulations are used to study ion and polymer chain dynamic properties in a
simplified lattice model with only one species of mobile ions. The ions interact attractively
with specific beads in the...
W Dieterich, O Dürr, P Pendzig, A Bunde, A Nitzan - Physica A: Statistical Mechanics and its Applications, 1999 - Elsevier
Abstract: Structural disorder is an inherent property of solid materials, which can support a macroscopic ionic current. Many transport phenomena in these solid ionic conductors...
M Aytimur, S Reiner, L Wörteler, T Chondrogiannis, M Grossniklaus
Proceedings of the ACM on Management of Data, 2024
Abstract: Cardinality estimation is an important step in cost-based database query optimization. The accuracy of the estimates directly affects the ability of an optimizer to identify the most...
J Bornholdt, T Chondrogiannis, M Grossniklaus | arXiv preprint arXiv:2407.03112, 2024
Abstract: With recent sensor and tracking technology advances, the volume of available trajectory data is steadily increasing. Consequently, managing and analyzing trajectory data has seen significant interest from the research community. The challenges presented by...
S Reiner, M Grossniklaus | 2023
Abstract: In database systems, accurate cardinality estimation is a cornerstone of effective query optimization. In this context, estimators that use machine learning have shown significant promise. Despite their potential, the effectiveness of these learned estimators strongly depends on their ability...
T Chondrogiannis, M Grossniklaus
Proceedings of the 18th International Symposium on Spatial and Temporal Data, 2023
Abstract: Highways play a crucial role in transportation services as they facilitate long-distance traveling and allow driving at an almost constant speed, thus resulting in lower fuel consumption and emissions. Many...