Topological Data Analysis
125 papers with code • 0 benchmarks • 3 datasets
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Most implemented papers
Topological Data Analysis of Decision Boundaries with Application to Model Selection
We propose the labeled \v{C}ech complex, the plain labeled Vietoris-Rips complex, and the locally scaled labeled Vietoris-Rips complex to perform persistent homology inference of decision boundaries in classification tasks.
To Trust Or Not To Trust A Classifier
google/TrustScore
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NeurIPS 2018
Knowing when a classifier's prediction can be trusted is useful in many applications and critical for safely using AI.
Mapper Comparison with Wasserstein Metrics
The challenge of describing model drift is an open question in unsupervised learning.
Persistence Bag-of-Words for Topological Data Analysis
Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs).
Approximating Continuous Functions on Persistence Diagrams Using Template Functions
Specifically, we begin by characterizing relative compactness with respect to the bottleneck distance, and then provide explicit theoretical methods for constructing compact-open dense subsets of continuous functions on persistence diagrams.
Topology of Learning in Artificial Neural Networks
Understanding how neural networks learn remains one of the central challenges in machine learning research.
A topological data analysis based classification method for multiple measurements
Using data from 100 examples of each of 6 point processes, the classifier achieved 96. 8% accuracy.
Persistence Curves: A canonical framework for summarizing persistence diagrams
First, we develop a general and unifying framework of vectorizing diagrams that we call the \textit{Persistence Curves} (PCs), and show that several well-known summaries, such as Persistence Landscapes, fall under the PC framework.
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures
Persistence diagrams, the most common descriptors of Topological Data Analysis, encode topological properties of data and have already proved pivotal in many different applications of data science.
A Persistent Weisfeiler–Lehman Procedure for Graph Classification
The Weisfeiler–Lehman graph kernel exhibits competitive performance in many graph classification tasks.
