Automatic Design of Deep Neural Networks

The development of the last years shows that machine learning and especially the subarea of Deep Learning will be an important component in the future, both in the scientific and industrial area. In the BMBF project »Deep Topology Learning« we are working together with other institutes on the acceleration of design algorithms.

From speech recognition and automatic image analysis to prototypes of automatically moving cars or »Go« playing algorithms at world champion level: so-called deep learning processes are almost always behind the success stories. This family of learning methods typically uses overparameterized and usually very large artificial neural networks (DNN) to model the learning problems. The training of such networks requires not only very large amounts of data, but also enormous computing power. Despite the sometimes impressive results achieved with DNNs, they still have some disadvantages that currently often still hinder their broad use in practice: In addition to the typically required very large amounts of data, this is primarily the complex development process.

Design Algorithms Replace Trial and Error

The design of new, problem-specific network topologies is a very time-consuming and computationally intensive process. Until now, the development of new deep learning solutions has been carried out in a purely heuristic and experience-driven "trial-and-error" approach. The aim of the BMBF project "Deep Topology Learning" (DeToL) is to significantly accelerate and simplify this design process for deep learning solutions by means of automatic, data-driven design algorithms.