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Found 7 papers, 2 papers with code
Position Paper: Challenges and Opportunities in Topological Deep Learning
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models.
Genomics Data Analysis via Spectral Shape and Topology
Precisely, we show that a Gaussian mixture approximation method can be used to produce graphical structures that successfully separate tumor and healthy subjects, and produce two subgroups of tumor subjects.
A Random Persistence Diagram Generator
Topological data analysis (TDA) studies the shape patterns of data.
Materials Fingerprinting Classification
Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional coordinates.
Bayesian Topological Learning for Classifying the Structure of Biological Networks
In this work, we analyze and classify these filament networks by transforming them into persistence diagrams whose variability is quantified via a Bayesian framework on the space of persistence diagrams.
Bayesian Topological Learning for Brain State Classification
Investigation of human brain states through electroencephalograph (EEG) signals is a crucial step in human-machine communications.
A Bayesian Framework for Persistent Homology
In essence, we model persistence diagrams as Poisson point processes with prior intensities and compute posterior intensities by adopting techniques from the theory of marked point processes.
Methodology 62F15, 60G55, 62-07
