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3 papers
NervePool: A Simplicial Pooling Layer
Sarah McGuire Scullen, Ernst Röell, Elizabeth Munch +2
For deep learning problems on graph-structured data, pooling layers are important for down sampling, reducing computational cost, and to minimize overfitting. We define a pooling l…
Using topological data analysis to compare inter-subject variability across resting state functional MRI brain representations
Ty Easley, Kevin Freese, Elizabeth Munch +1
In neuroimaging, extensive post-processing of resting-state functional MRI (rfMRI) data is necessary for its application and investigation in relation to brain-behavior association…
Bounding the Interleaving Distance for Mapper Graphs with a Loss Function
Erin W. Chambers, Elizabeth Munch, Sarah Percival +1
Data consisting of a graph with a function mapping into arise in many data applications, encompassing structures such as Reeb graphs, geometric graphs, and knot embe…