TopoFisher optimizes trainable filtrations, vectorizations, and compressors in persistent homology to maximize Fisher information, yielding higher information than fixed cosmological summaries and approaching neural baselines with far fewer parameters while generalizing better under simulator shifts
Frontiers in Artificial Intelligence , VOLUME=
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A neural network maps one image to a chiral spin texture whose skyrmion number equals the Euler characteristic, refined by exchange, DM, and anisotropy terms in the loss.
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TopoFisher: Learning Topological Summary Statistics by Maximizing Fisher Information
TopoFisher optimizes trainable filtrations, vectorizations, and compressors in persistent homology to maximize Fisher information, yielding higher information than fixed cosmological summaries and approaching neural baselines with far fewer parameters while generalizing better under simulator shifts
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Predicting Euler Characteristics and Constructing Topological Structure Using Machine Learning Techniques
A neural network maps one image to a chiral spin texture whose skyrmion number equals the Euler characteristic, refined by exchange, DM, and anisotropy terms in the loss.