Riemannian optimization

20 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.

Riemannian Adaptive Optimization Methods

geoopt/geoopt ICLR 2019

Several first order stochastic optimization methods commonly used in the Euclidean domain such as stochastic gradient descent (SGD), accelerated gradient descent or variance reduced methods have already been adapted to certain Riemannian settings.

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.

Geometric Mean Metric Learning

PouriaZ/GMML 18 Jul 2016

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

Riemannian Optimization for Skip-Gram Negative Sampling

AlexGrinch/ro_sgns ACL 2017

Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in "word2vec" software, is usually optimized by stochastic gradient descent.

An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic Riemannian Optimization

utvisionlab/mixest 10 Jun 2017

This motivates us to take a closer look at the problem geometry, and derive a better formulation that is much more amenable to Riemannian optimization.

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

rballester/ttrecipes 30 Aug 2017

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1.