Workshop on Mathematics of Deep Learning 2019
Deep Learning has evolved into one of the hot topics in industry and science with a wide range of applications related to the processing and interpretation of large amounts of data. This includes recommendation and chat systems, image and language recognition, classification, identification, knowledge discovery, simulation of complex physical phenomena, autonomous systems, and many other areas.
While the success of recent neural network architectures has been breathtaking, the mathematical understanding of these architectures is still in its infancy. However, mathematical disciplines have recently began to embrace this challenge and tremendous progress has already been made, for instance regarding the analysis of the expressive power, approximation properties, complexities and the stochastic training. With such insights, one can hope to further the development of more efficient algorithms, improved architectures and the understanding of the reliability of these modern methods.
The aim of the workshop is to bring together leading researchers with a focus on the mathematical analysis and interpretation of current Machine Learning approaches. While Deep Learning with Artificial Neural Networks seems to be the most popular research area, alternative methods and representations will also be presented.
Invited Speakers (to be updated)
We gratefully acknowledge support by Berlin Mathematics Research Center MATH+.
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