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TopoFisher learns topological summaries by maximizing Fisher information

By PulseAugur Editorial · [2 sources] ·

Researchers have developed TopoFisher, a novel differentiable pipeline that learns topological summaries by maximizing Fisher information. This method optimizes trainable filtrations, vectorizations, and compressors without requiring posterior samples or supervised targets, while maintaining topological inductive bias. Experiments show TopoFisher outperforms fixed topological vectorizations and achieves higher Fisher information than state-of-the-art cosmological summaries in complex inference problems like weak gravitational lensing, using significantly fewer parameters. AI

IMPACT Introduces a parameter-efficient method for simulation-based inference, potentially improving accuracy in complex cosmological and scientific modeling.

RANK_REASON Academic paper detailing a new method for learning topological summary statistics.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

TopoFisher learns topological summaries by maximizing Fisher information

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Academic paper detailing a new method for learning topological summary statistics.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Matteo Biagetti, Mathieu Carri\`ere, Francesco Conti, Enrico Maria Ferrari, Sven Heydenreich, Karthik Viswanathan ·

    TopoFisher: Learning Topological Summary Statistics by Maximizing Fisher Information

    arXiv:2605.07720v1 Announce Type: new Abstract: Persistence diagrams provide stable, interpretable summaries of geometric and topological structure and are useful for simulation-based inference when low-order statistics miss key information. Yet persistence-based pipelines requir…

  2. arXiv stat.ML TIER_1 English(EN) · Karthik Viswanathan ·

    TopoFisher: Learning Topological Summary Statistics by Maximizing Fisher Information

    Persistence diagrams provide stable, interpretable summaries of geometric and topological structure and are useful for simulation-based inference when low-order statistics miss key information. Yet persistence-based pipelines require hand-chosen filtrations, vectorizations, and c…

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