194 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Laboratoire de Mathématiques d'OrsayFR12 papers
- Institut national de recherche en sciences et technologies du numériqueFR9 papers
- Centre Inria de SaclayFR7 papers
- Laboratoire de Probabilités, Statistique et ModélisationFR6 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- Université Paris-SaclayFR4 papers
- Centre Inria d'Université Côte d'AzurFR3 papers
- École Polytechnique Fédérale de LausanneCH3 papers
- Institut de Mathématiques de ToulouseFR3 papers
- Institut National des Sciences Appliquées de ToulouseFR3 papers
- Université Toulouse III - Paul SabatierFR3 papers
- University of PotsdamDE3 papers
5 papers · 1 filter
Patchworking real algebraic hypersurfaces with asymptotically large Betti numbers
Charles Arnal
In this article, we describe a recursive method for constructing a family of real projective algebraic hypersurfaces in ambient dimension from families of such hypersurfaces in…
Generalized Penalty for Circular Coordinate Representation
Hengrui Luo, Alice Patania, Jisu Kim +1
Topological Data Analysis (TDA) provides novel approaches that allow us to analyze the geometrical shapes and topological structures of a dataset. As one important application, TDA…
Computing persistent Stiefel-Whitney classes of line bundles
Raphaël Tinarrage
We propose a definition of persistent Stiefel-Whitney classes of vector bundle filtrations. It relies on seeing vector bundles as subsets of some Euclidean spaces. The usual Čech f…
PLLay: Efficient Topological Layer based on Persistence Landscapes
Kwangho Kim, Jisu Kim, Manzil Zaheer +3
We propose PLLay, a novel topological layer for general deep learning models based on persistence landscapes, in which we can efficiently exploit the underlying topological feature…
Optimal quantization of the mean measure and applications to statistical learning
Frédéric Chazal, Clément Levrard, Martin Royer
This paper addresses the case where data come as point sets, or more generally as discrete measures. Our motivation is twofold: first we intend to approximate with a compactly supp…