Riemannian optimization

30 papers with code • 0 benchmarks • 0 datasets

Optimization methods on Riemannian manifolds.


Use these libraries to find Riemannian optimization models and implementations

Most implemented papers

Poincaré Embeddings for Learning Hierarchical Representations

facebookresearch/poincare-embeddings NeurIPS 2017

Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs.

Exponential Machines

Bihaqo/exp-machines 12 May 2016

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 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 6 May 2020

Geoopt is a research-oriented modular open-source package for Riemannian Optimization in PyTorch.

Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach

fedelopez77/sympa 9 Jun 2021

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

utvisionlab/mixest 22 Jul 2015

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

pymanopt/pymanopt 10 Mar 2016

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

hiroyuki-kasai/RSOpt 24 May 2016

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

PouriaZ/GMML 18 Jul 2016

We revisit the task of learning a Euclidean metric from data.

Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport

hiroyuki-kasai/RSOpt 18 Feb 2017

In recent years, stochastic variance reduction algorithms have attracted considerable attention for minimizing the average of a large but finite number of loss functions.