3 papers
stat.ML2026
Unreduced Persistence Diagrams for Topological Machine Learning
Nicole Abreu, Parker B. Edwards, Francis Motta
Supervised machine learning pipelines trained on features derived from persistent homology have been experimentally observed to ignore much of the information contained in a persis…
stat.ML2024
A Pipeline for Data-Driven Learning of Topological Features with Applications to Protein Stability Prediction
Amish Mishra, Francis Motta
In this paper, we propose a data-driven method to learn interpretable topological features of biomolecular data and demonstrate the efficacy of parsimonious models trained on topol…
q-bio.PE2024
Generalized Measures of Population Synchrony
Francis C. Motta, Kevin McGoff, Breschine Cummins +1
Synchronized behavior among individuals is a ubiquitous feature of populations. Understanding mechanisms of (de)synchronization demands meaningful, interpretable, computable quanti…