Quick Overview: In this talk I will provide a birds-eye-view of some results in Part of the Physics for AI Workshop, Oxford, 10-21 March 2025, physics4ai.web.ox.ac.uk. Uh a few caveats and comments from the modern era of thinking about

James Halverson Machine Learning For - Detailed Overview & Context

In this talk I will provide a birds-eye-view of some results in Part of the Physics for AI Workshop, Oxford, 10-21 March 2025, physics4ai.web.ox.ac.uk. Uh a few caveats and comments from the modern era of thinking about Abstract: Neural networks provide state-of-the-art approximations of Calabi-Yau metrics. In this talk I'll broaden these results and ... Yeah that's right 25 years the the paper that i Abstract: We propose a theoretical understanding of neural networks in terms of Wilsonian effective field theory.

James Halverson - Ricci Flow with Infinite Neural Networks

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James Halverson | Machine Learning for Mathematics
Machine Learning: Where to Apply in Theoretical Physics by Jim Halverson
Jim Halverson: Field Theory for Machine Learning
Jim Halverson - Machine Learning for the Landscape (Landscapia)
James Halverson - Complexity of Machine Learning and Landscapes
Jim Halverson: Metric Flows and Calabi–Yau Metrics with Infinite-Width Neural Networks
Jim Halverson: Neural Networks for Field Theory
Statistics and Symmetries of Neural Networks and Quantum Fields
Jim Halverson Lecture 1 on String Remnants
Jim Halverson (Northeastern): "Neural Networks and Quantum Field Theory
James Halverson | Sparsity and Symbols with Kolmogorov-Arnold Networks
Survey Lecture 4: Jim Halverson
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