MAPLE estimates conditional class probabilities by local averaging over Mapper-graph neighborhoods with data-driven cover selection and proves consistency under regularity conditions.
Nature neuroscience , volume=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
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2026 3representative citing papers
Authors create a benchmark across discrete/continuous and static/dynamical systems and introduce the Causal Abstraction Error (CAE) metric that reliably distinguishes valid from invalid causal abstractions when it includes faithfulness testing.
Decoding alignment metrics can remain high and unchanged even when encoding manifold topology is causally altered, so they do not imply similar function or computation across neural populations.
citing papers explorer
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MAPLE: Mapper Based Localized Prediction with Data Driven Cover Selection for High dimensional Data
MAPLE estimates conditional class probabilities by local averaging over Mapper-graph neighborhoods with data-driven cover selection and proves consistency under regularity conditions.
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Validating Causal Abstraction Metrics on Simulated Complex Systems
Authors create a benchmark across discrete/continuous and static/dynamical systems and introduce the Causal Abstraction Error (CAE) metric that reliably distinguishes valid from invalid causal abstractions when it includes faithfulness testing.
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Decoding Alignment without Encoding Alignment: A critique of similarity analysis in neuroscience
Decoding alignment metrics can remain high and unchanged even when encoding manifold topology is causally altered, so they do not imply similar function or computation across neural populations.