Deep assignment flows for structured data labeling: design, learning and prediction performance

• Applicant: Professor Dr. Christoph Schnörr
• Subject Area: Mathematics ; Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 463952752

This project focuses on a class of continuous-time neural ordinary differential equations for labeling metric data on graphs, in order to contribute to the theory of deep learning from three viewpoints: (i) use of information geometry for the design and understanding of deep networks in connection with structured prediction and learning; (ii) geometric characterization the dynamics of parameter learning and the interaction with state space evolutions as model of contextual decisions; (iii) study of PAC-Bayes risk bounds which quantify the performance of classification and label prediction by deep assignment flows.

• DFG Programme: Priority Programmes
• Subproject of: SPP 2298: Theoretical Foundations of Deep Learning