Meta-learning for regularizing deep networks under small data regimes (C05)

• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 499552394

We aim at extending the advances made for rather large tabular datasets to "scale down” deep learning to be effective also in the regime of small datasets. Specifically, we will develop approaches to search for optimal combinations of regularization methods, based on a meta-learning approach across many small datasets and by ensembling different combinations of regularization methods. We will also tackle the more structured data modalities of longitudinal data and image data.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1597: Small Data
• Applicant Institution: Albert-Ludwigs-Universität Freiburg
• Project Heads: Professor Dr. Josif Grabocka; Professor Frank Hutter, Ph.D.