Persistent Homology with Improved Locality Information for more Effective Delineation

no code implementations • 12 Oct 2021 • Doruk Oner, Adélie Garin, Mateusz Koziński, Kathryn Hess, Pascal Fua

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

no code implementations • 29 Sep 2021 • Doruk Oner, Adélie Garin, Mateusz Kozinski, Kathryn Hess, Pascal Fua

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.

Deep Learning

giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration

1 code implementation • NeurIPS Workshop TDA_and_Beyond 2020 • Guillaume Tauzin, Umberto Lupo, Lewis Tunstall, Julian Burella Pérez, Matteo Caorsi, Wojciech Reise, Anibal Medina-Mardones, Alberto Dassatti, Kathryn Hess

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.

BIG-bench Machine Learning Topological Data Analysis

Two-Tier Mapper: a user-independent clustering method for global gene expression analysis based on topology

no code implementations • 21 Dec 2017 • Rachel Jeitziner, Mathieu Carrière, Jacques Rougemont, Steve Oudot, Kathryn Hess, Cathrin Brisken

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.

Clustering