Riemannian optimization
39 papers with code • 0 benchmarks • 0 datasets
Optimization methods on Riemannian manifolds.
Benchmarks
These leaderboards are used to track progress in Riemannian optimization
Libraries
Use these libraries to find Riemannian optimization models and implementationsMost implemented papers
Poincaré Embeddings for Learning Hierarchical Representations
facebookresearch/poincare-embeddings
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NeurIPS 2017
Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs.
Exponential Machines
Modeling interactions between features improves the performance of machine learning solutions in many domains (e. g. recommender systems or sentiment analysis).
geomstats: a Python Package for Riemannian Geometry in Machine Learning
geomstats/geomstats
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ICLR 2019
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.
Geoopt: Riemannian Optimization in PyTorch
geoopt/geoopt
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Geoopt is a research-oriented modular open-source package for Riemannian Optimization in PyTorch.
Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach
We propose the systematic use of symmetric spaces in representation learning, a class encompassing many of the previously used embedding targets.
MixEst: An Estimation Toolbox for Mixture Models
Mixture models are powerful statistical models used in many applications ranging from density estimation to clustering and classification.
Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation
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.
Riemannian stochastic variance reduced gradient on Grassmann 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.
Geometric Mean Metric Learning
We revisit the task of learning a Euclidean metric from data.
Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport
In recent years, stochastic variance reduction algorithms have attracted considerable attention for minimizing the average of a large but finite number of loss functions.
