Project Details
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
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