Matthias Hein – författare

This book offers a comprehensive exploration of the theoretical foundations of deep learning, bridging the gap between its ever increasing empirical success and rigorous mathematical understanding. With a focus on interdisciplinary approaches, it illuminates the interplay between mathematics, statistics, and computer science to address pressing challenges in deep learning research. While deep learning continues to revolutionize industries such as healthcare, gaming, and autonomous systems, its applications in solving mathematical problems like inverse problems and partial differential equations mark a paradigm shift in scientific inquiry. Yet, this rapid progress often outpaces our theoretical understanding, leaving critical questions about expressivity, optimization, robustness, and fairness unanswered.This book aims to close this gap by presenting perspectives from mathematics, statistics, and applications. This interdisciplinary endeavor draws upon diverse mathematical frameworks, including algebraic geometry, differential geometry, stochastics, functional analysis, and topology, alongside fundamental tools from statistics and theoretical computer science. By synthesizing these fields, the book not only advances the theoretical foundation of deep learning but also sets the stage for novel applications across scientific domains. Targeted at researchers, advanced students, and professionals in mathematics, computer science, and related fields, this book serves as both an introduction to emerging theoretical insights and a roadmap for interdisciplinary collaboration. It is will be of value to anyone seeking to understand or to contribute to the future of deep learning.

The book develops a comprehensive understanding of the surface impedance of the oxide high-temperature superconductors in comparison with the conventional superconductor Nb3Sn. Linear and nonlinear microwave responses are treated separately, both in terms of models, theories or numerical approaches and in terms of experimental results. The theoretical treatment connects fundamental aspects of superconductivity to the specific high-frequency properties. The experimental data review the state of the art, as reported by many international groups. The book describes further the main features of appropriate preparation, handling, mounting, and refrigeration techniques, and finally discusses possible applications in passive and active microwave devices.

The book develops a comprehensive understanding of the surface impedance of the oxide high-temperature superconductors in comparison with the conventional superconductor Nb3Sn. Linear and nonlinear microwave responses are treated separately, both in terms of models, theories or numerical approaches and in terms of experimental results. The theoretical treatment connects fundamental aspects of superconductivity to the specific high-frequency properties. The experimental data review the state of the art, as reported by many international groups. The book describes further the main features of appropriate preparation, handling, mounting, and refrigeration techniques, and finally discusses possible applications in passive and active microwave devices.

This book constitutes the refereed proceedings of the 35th German Conference on Pattern Recognition, GCPR 2013, held in Saarbrücken, Germany, in September 2013. The 22 revised full papers and 18 revised poster papers were carefully reviewed and selected from 79 submissions. The papers covers topics such as image processing and computer vision, machine learning and pattern recognition, mathematical foundations, statistical data analysis and models, computational photography and confluence of vision and graphics, and applications in natural sciences, engineering, biomedical data analysis, imaging, and industry.