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Found 4 papers, 1 papers with code
Persistent Homology with Improved Locality Information for more Effective Delineation
Persistent Homology (PH) has been successfully used to train networks to detect curvilinear structures and to improve the topological quality of their results.
Localized Persistent Homologies for more Effective Deep Learning
Persistent Homologies have been successfully used to increase the performance of deep networks trained to detect curvilinear structures and to improve the topological quality of the results.
giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration
We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ implementations.
Two-Tier Mapper: a user-independent clustering method for global gene expression analysis based on topology
We have developed a topology-based analysis tool called Two-Tier Mapper (TTMap) to detect subgroups in global gene expression datasets and identify their distinguishing features.
