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 (nODEs) 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 design and understanding the role of parameters in connection with learning and structured prediction; (ii) study of PAC-Bayes risk bounds for local predictions of weight parameter patches on a manifold and the implication for the statistical accuracy of non-local labelings predicted by the nODE; (iii) algorithm design for parameter learning based on a linear tangent space representation of the nODE, as a basis for linking statistical learning theory to applications of assignment flows in practice.

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