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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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
Several terms in the third-order slow-roll power spectra are incorrect because three-dimensional integrals were evaluated by integrating a truncated Taylor expansion instead of Taylor-expanding the integral.
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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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Comment on: "Third-order corrections to the slow-roll expansion: Calculation and constraints with Planck, ACT, SPT, and BICEP/Keck [2025 PDU 47 101813]"
Several terms in the third-order slow-roll power spectra are incorrect because three-dimensional integrals were evaluated by integrating a truncated Taylor expansion instead of Taylor-expanding the integral.