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# Topological Data Analysis Edit

16 papers with code · Graphs

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# To Trust Or Not To Trust A Classifier

Knowing when a classifier's prediction can be trusted is useful in many applications and critical for safely using AI.

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# Deep Learning with Topological Signatures

Inferring topological and geometrical information from data can offer an alternative perspective on machine learning problems.

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# Persistence Images: A Stable Vector Representation of Persistent Homology

22 Jul 2015scikit-tda/persim

We convert a PD to a finite-dimensional vector representation which we call a persistence image (PI), and prove the stability of this transformation with respect to small perturbations in the inputs.

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# PersLay: A Simple and Versatile Neural Network Layer for Persistence Diagrams

20 Apr 2019MathieuCarriere/perslay

In order to exploit topological information from graph data, we show how graph structures can be encoded in the so-called extended persistence diagrams computed with the heat kernel signatures of the graphs.

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# A Persistent Weisfeiler–Lehman Procedure for Graph Classification

The Weisfeiler–Lehman graph kernel exhibits competitive performance in many graph classification tasks.

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# Topology of Learning in Artificial Neural Networks

21 Feb 2019maximevictor/topo-learning

Understanding how neural networks learn remains one of the central challenges in machine learning research.

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# A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams

28 May 2016rushilanirudh/pdsphere

This paper concerns itself with one popular topological feature, which is the number of $d-$dimensional holes in the dataset, also known as the Betti$-d$ number.

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# Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications

19 Jul 2019rushilanirudh/icf-jag-cycleGAN

With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization.

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# Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology

While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data.

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# Persistence Bag-of-Words for Topological Data Analysis

21 Dec 2018bziiuj/pcodebooks

Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs).

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