4 papers · 1 filter
Contact Wasserstein Geodesics for Non-Conservative Schrödinger Bridges
Andrea Testa, Søren Hauberg, Tamim Asfour +1
The Schrödinger Bridge provides a principled framework for modeling stochastic processes between distributions; however, existing methods are limited by energy-conservation assump…
The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs
Peter Mostowsky, Vincent Dutordoir, Iskander Azangulov +6
Kernels are a fundamental technical primitive in machine learning. In recent years, kernel-based methods such as Gaussian processes are becoming increasingly important in applicati…
On Probabilistic Pullback Metrics for Latent Hyperbolic Manifolds
Luis Augenstein, Noémie Jaquier, Tamim Asfour +1
Probabilistic Latent Variable Models (LVMs) excel at modeling complex, high-dimensional data through lower-dimensional representations. Recent advances show that equipping these la…
Riemann: Learning Riemannian Submanifolds from Riemannian Data
Leonel Rozo, Miguel González-Duque, Noémie Jaquier +1
Latent variable models are powerful tools for learning low-dimensional manifolds from high-dimensional data. However, when dealing with constrained data such as unit-norm vectors o…