This project is to compare our Fisher-Rao registration framework to the Dynamic Time Warping (DTW) to analyze neural signals such as EEG and fMRI where phase variability plays an important role in the data. The code provided here illustrate comparisons with simulation examples.
Geometric AI architectures combining spectral analysis, information geometry, and zeta function theory. Democratizing advanced mathematics through practical implementation.
Curriculum learning framework that uses geometrically structured datasets and lets the model self discover learning path through emergent dynamics
Information-geometric early warning signals: KL divergence rate, Fisher-Rao distance, geodesic acceleration
A Numba-accelerated Python library that renders exact Fisher-Rao information geometry as archival NetCDF, PNG diagnostics, and animated GIFs across four cases spanning hyperbolic, spherical, dually flat, and diffusive structure.
Information geometry on the probability simplex
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