The theory part of S3AI comprises mathematical aspects of transfer learning and a novel computational geometric approach for deep model analysis, e.g.

  • Tessellation: study the interdependence between a deep model represented as neural network, its induced (tessellation) geometry in the input space and its separability properties;
  • Deep Transfer Learning: provide quantitative bounds on the misclassification and dertermine convergence rates in transfer learning settings;