- ホーム
- > 洋書
- > ドイツ書
- > Mathematics, Sciences & Technology
- > Mathematics
- > probability calculus, stochastics, mathematical statistics
Full Description
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.
Contents
Part I. Expressivity, Optimization and Generalization.- Chapter 1. Constraining the outputs of ReLU neural networks.- Chapter 2. Implicit Bias for (Complex) Diagonal Linear Networks.- Chapter 3. A Dynamical Systems Perspective on the Analysis of Neural Networks.- Chapter 4. High-dimensional feedback control with deep learning.- Chapter 5. Meanfield Analysis for Interacting Particle Systems in Machine Learning Applications.- Chapter 6. Reproducing Kernel Hilbert Spaces for Neural Networks.- Chapter 7. On the Convergence of Variational Deep Learning to Entropy Sums.- Chapter 8. Pontryagin Maximum Principles for Training Neural ODEs.- Chapter 9. Theoretical Foundations of Representation Learning using Unlabeled Data: Statistics and Optimization.- Chapter 10. Deep Assignment Flows for Structured Prediction: Recent Results.- Chapter 11. Multilevel Architectures and Algorithms in Deep Learning.- Chapter 12. Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale using Sketched Methods.- Part II. Safety, Robustness, Interpretability and Fairness.- Chapter 13. A Unified Framework for Adversarial Generalization in Machine Learning.- Chapter 14. Adversarially Robust Foundation Models.- Chapter 15. Provable Robustness Certification of Graph Neural Networks.- Chapter 16. Explainable Learning Based Regularization of Inverse Problems.- Chapter 17. Provable Mixed-Noise Learning with Flow-Matching.- Chapter 18. A Tight Approximability Analysis of Boosting.- Part III. Inverse Problems and Partial Differential Equations.- Chapter 19. End-to-End Neural Networks for Inverse Problems: Performance and Robustness Guarantees.- Chapter 20. A neural operator view on U-Nets for inverse imaging problems.- Chapter 21. Structure Preserving Surrogate Models for the Boltzmann Equation.- Chapter 22. Harmonic Artificial Intelligence based on Linear Operators.- Chapter 23. Curse of dimensionality-free nonlinear Monte Carlo and deep
neural network approximations for MDPs and semilinear PDEs:existence results and limitations.- Chapter 24. Neural and tensor networks for high-dimensional parametric PDEs
and sampling.- Chapter 25. Error analysis for the deep Kolmogorov method.
