Deep learning in space and time (A05)

• Subject Area: Statistics and Econometrics ; Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing ; Mathematics
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 520388526

Deep learning in space and time aims for a better theoretical understanding of deep neural networks for space-time data, which will result in improved predictions for spatio-temporal data including statistical guarantees. In the long run, deep-learning approaches will be developed to meet demands such as interpretability, statistical inference and statistical optimality.

• DFG Programme: CRC/Transregios
• Subproject of: TRR 391: Spatio-temporal Statistics for the Transition of Energy and Transport
• Applicant Institution: Technische Universität Dortmund
• Project Heads: Professorin Dr. Asja Fischer, since 7/2024; Professor Dr. Johannes Lederer, since 7/2024; Professorin Dr. Hanna Meyer, since 7/2024