Mathieu Carrière | DeepAI

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∙ 06/19/2023

A Framework for Fast and Stable Representations of Multiparameter Persistent Homology Decompositions

Topological data analysis (TDA) is an area of data science that focuses ...

0 David Loiseaux, et al. ∙

∙ 06/06/2023

Stable Vectorization of Multiparameter Persistent Homology using Signed Barcodes as Measures

Persistent homology (PH) provides topological descriptors for geometric ...

0 David Loiseaux, et al. ∙

∙ 05/22/2023

MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Deep Neural Networks

Despite their successful application to a variety of tasks, neural netwo...

0 Felix Hensel, et al. ∙

∙ 06/04/2022

Efficient Approximation of Multiparameter Persistence Modules

Topological Data Analysis is a growing area of data science, which aims ...

0 David Loiseaux, et al. ∙

∙ 02/03/2022

RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds

The use of topological descriptors in modern machine learning applicatio...

0 Thibault de Surrel, et al. ∙

∙ 09/01/2021

A Gradient Sampling Algorithm for Stratified Maps with Applications to Topological Data Analysis

We introduce a novel gradient descent algorithm extending the well-known...

0 Jacob Leygonie, et al. ∙

∙ 06/14/2021

Topology identifies emerging adaptive mutations in SARS-CoV-2

The COVID-19 pandemic has lead to a worldwide effort to characterize its...

0 Michael Bleher, et al. ∙

∙ 05/07/2021

Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs

Although neural networks are capable of reaching astonishing performance...

0 Théo Lacombe, et al. ∙

∙ 10/16/2020

Optimizing persistent homology based functions

Solving optimization tasks based on functions and losses with a topologi...

0 Mathieu Carrière, et al. ∙

∙ 01/06/2020

MREC: a fast and versatile framework for aligning and matching point clouds with applications to single cell molecular data

Comparing and aligning large datasets is a pervasive problem occurring a...

0 Andrew J. Blumberg, et al. ∙

∙ 01/06/2020

MREC: a fast and versatile framework for aligning and matching data with applications to single cell molecular data

Comparing and aligning large datasets is a pervasive problem occurring a...

0 Andrew J. Blumberg, et al. ∙

∙ 12/23/2019

Approximation of Reeb spaces with Mappers and Applications to Stochastic Filters

Reeb spaces, as well as their discretized versions called Mappers, are c...

3 Mathieu Carrière, et al. ∙

∙ 04/20/2019

PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures

Persistence diagrams, the most common descriptors of Topological Data An...

0 Mathieu Carrière, et al. ∙

∙ 04/20/2019

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

Persistence diagrams, a key descriptor from Topological Data Analysis, e...

0 Mathieu Carrière, et al. ∙

∙ 04/20/2019

A General Neural Network Architecture for Persistence Diagrams and Graph Classification

Graph classification is a difficult problem that has drawn a lot of atte...

0 Mathieu Carrière, et al. ∙

∙ 12/04/2018

Topological Data Analysis of Single-cell Hi-C Contact Maps

In this article, we show how the recent statistical techniques developed...

0 Mathieu Carrière, et al. ∙

∙ 06/19/2018

On the Metric Distortion of Embedding Persistence Diagrams into separable Hilbert spaces

Persistence diagrams are important descriptors in Topological Data Analy...

0 Mathieu Carrière, et al. ∙

∙ 06/19/2018

On the Metric Distortion of Embedding Persistence Diagrams into Reproducing Kernel Hilbert Spaces

Persistence diagrams are important feature descriptors in Topological Da...

0 Mathieu Carrière, et al. ∙

∙ 06/11/2017

Sliced Wasserstein Kernel for Persistence Diagrams

Persistence diagrams (PDs) play a key role in topological data analysis ...

0 Mathieu Carrière, et al. ∙

∙ 06/01/2017

Statistical Analysis and Parameter Selection for Mapper

In this article, we study the question of the statistical convergence of...

0 Mathieu Carrière, et al. ∙

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