Mathematical Foundations of Machine Learning: PDEs, Probability, and Dynamics

This workshop aims to bring together researchers at the intersection of mathematics and modern machine learning, with a particular emphasis on foundational perspectives. Topics will span generative modeling, large language models, and neural network training, highlighting the essential roles played by partial differential equations (PDEs), dynamical systems, and probability theory.

Our goal is to foster dialogue between communities in analysis, probability, dynamical systems and machine learning, and to explore how mathematical structure can enhance the understanding of modern paradigms.

TBP

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