Associated Projects

Here is the list of other associated projects that are relevant to the research questions under the frame of the ONE MUNICH project, but are funded from different resources.

1. Limitations of Deep Neural Networks

The success of Deep Learning in many different practical application fields, ranging from image classification, protein folding prediction to natural language processing, lead to the (ongoing) development of a rich mathematical theory. Although great strides have been made to deepen our understanding of the field, many questions remain open at the moment. One of the most important but also most fundamental issues concerns the capabilities and limitations of Deep Learning. Simply put, which problems can we reasonably expect Deep Learning to solve and where can we, with great certainty, predict failures of Deep Learning methods. An often neglected aspect of this discussion are the restrictions imposed by the hardware the systems are running on. Deep Learning methods can not exceed the fundamental barriers of its computation platforms. Because of this it is crucial to link the capabilities of Deep Learning to (actual or theoretical) computation devices. The aim of this project is to characterize the possibilities and boundaries of Deep Learning inflicted by computational limits.

Supervising PIs:

  • Prof. Dr. Holger Boche (TUM – EI) – website
  • Prof. Dr. Gitta Kutyniok (LMU – MATH) – website