9 citations · 11 across the 7 of their papers we have counts for
10 papers
Regression-Based Elastic Metric Learning on Shape Spaces of Elastic Curves
Adele Myers, Nina Miolane
We propose a metric learning paradigm, Regression-based Elastic Metric Learning (REML), which optimizes the elastic metric for geodesic regression on the manifold of discrete curve…
Parametric information geometry with the package Geomstats
Alice Le Brigant, Jules Deschamps, Antoine Collas +1
We introduce the information geometry module of the Python package Geomstats. The module first implements Fisher-Rao Riemannian manifolds of widely used parametric families of prob…
Testing geometric representation hypotheses from simulated place cell recordings
Thibault Niederhauser, Adam Lester, Nina Miolane +2
Hippocampal place cells can encode spatial locations of an animal in physical or task-relevant spaces. We simulated place cell populations that encoded either Euclidean- or graph-b…
Heterogeneous reconstruction of deformable atomic models in Cryo-EM
Youssef Nashed, Ariana Peck, Julien Martel +6
Cryogenic electron microscopy (cryo-EM) provides a unique opportunity to study the structural heterogeneity of biomolecules. Being able to explain this heterogeneity with atomic mo…
Intentional Choreography with Semi-Supervised Recurrent VAEs
Mathilde Papillon, Mariel Pettee, Nina Miolane
We summarize the model and results of PirouNet, a semi-supervised recurrent variational autoencoder. Given a small amount of dance sequences labeled with qualitative choreographic…
ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results
Nina Miolane, Matteo Caorsi, Umberto Lupo +30
This paper presents the computational challenge on differential geometry and topology that happened within the ICLR 2021 workshop "Geometric and Topological Representation Learning…