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#120 - Iolo Jones: Riemannian Diffusion Geometry, Geometric Data Analysis, Markov Chains

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Optimizing Computation in High Dimensions

This chapter examines the relationship between gradient descent, diffusion processes, and data geometry. It emphasizes the importance of learning rates and step selection in high-dimensional spaces while exploring theoretical concepts that could improve computational efficiency.

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