7 citations · 8 across the 8 of their papers we have counts for
4 papers · 1 filter
Revisiting the Sliced Wasserstein Kernel for persistence diagrams: a Figalli-Gigli approach
Marc Janthial, Théo Lacombe
The Sliced Wasserstein Kernel (SWK) for persistence diagrams was introduced in (Carri{è}re et al. 2017) as a powerful tool to implicitly embed persistence diagrams in a Hilbert spa…
Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs
Théo Lacombe, Yuichi Ike, Mathieu Carriere +3
Although neural networks are capable of reaching astonishing performances on a wide variety of contexts, properly training networks on complicated tasks requires expertise and can…
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures
Mathieu Carrière, Frédéric Chazal, Yuichi Ike +3
Persistence diagrams, the most common descriptors of Topological Data Analysis, encode topological properties of data and have already proved pivotal in many different applications…
Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport
Théo Lacombe, Marco Cuturi, Steve Oudot
Persistence diagrams (PDs) are now routinely used to summarize the underlying topology of complex data. Despite several appealing properties, incorporating PDs in learning pipeline…