Geometric Learning Dynamics
This work provides a foundational theory for understanding learning processes across diverse domains, potentially impacting all of ML/AI, but it is a new paradigm rather than incremental.
The paper tackles the problem of modeling learning dynamics across physical, biological, and machine learning systems by developing a unified geometric framework that identifies three fundamental regimes based on a power-law relationship between metric tensor and noise covariance, revealing that an intermediate regime (α=1/2) enables very fast machine learning algorithms and underlies biological complexity.
We present a unified geometric framework for modeling learning dynamics in physical, biological, and machine learning systems. The theory reveals three fundamental regimes, each emerging from the power-law relationship $g \propto κ^α$ between the metric tensor $g$ in the space of trainable variables and the noise covariance matrix $κ$. The quantum regime corresponds to $α= 1$ and describes Schrödinger-like dynamics that emerges from a discrete shift symmetry. The efficient learning regime corresponds to $α= \tfrac{1}{2}$ and describes very fast machine learning algorithms. The equilibration regime corresponds to $α= 0$ and describes classical models of biological evolution. We argue that the emergence of the intermediate regime $α= \tfrac{1}{2}$ is a key mechanism underlying the emergence of biological complexity.