30 papers with code • 0 benchmarks • 0 datasets
Optimization methods on Riemannian manifolds.
These leaderboards are used to track progress in Riemannian optimization
LibrariesUse these libraries to find Riemannian optimization models and implementations
This paper also presents a review of manifolds in machine learning and an overview of the geomstats package with examples demonstrating its use for efficient and user-friendly Riemannian geometry.
We propose the systematic use of symmetric spaces in representation learning, a class encompassing many of the previously used embedding targets.
Optimization on manifolds is a class of methods for optimization of an objective function, subject to constraints which are smooth, in the sense that the set of points which satisfy the constraints admits the structure of a differentiable manifold.
In this paper, we propose a novel Riemannian extension of the Euclidean stochastic variance reduced gradient algorithm (R-SVRG) to a compact manifold search space.
In recent years, stochastic variance reduction algorithms have attracted considerable attention for minimizing the average of a large but finite number of loss functions.