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Riemannian optimization

Optimization methods on Riemannian manifolds.

Papers

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Showing 1–50 of 153 papers

TitleStatusHype
Geoopt: Riemannian Optimization in PyTorchCode2
Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian ApproachCode2
Geomstats: A Python Package for Riemannian Geometry in Machine LearningCode2
Riemannian Adaptive Optimization MethodsCode2
geomstats: a Python Package for Riemannian Geometry in Machine LearningCode2
Riemannian Optimization on Relaxed Indicator Matrix ManifoldCode2
FireANTs: Adaptive Riemannian Optimization for Multi-Scale Diffeomorphic MatchingCode2
Poincaré Embeddings for Learning Hierarchical RepresentationsCode1
Fast hyperboloid decision tree algorithmsCode1
Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency AnalysisCode1
Projective Manifold Gradient Layer for Deep Rotation RegressionCode1
Tensor-Train Density EstimationCode1
Contrastive Video Representation Learning via Adversarial Perturbations—0
A Manifold Approach to Learning Mutually Orthogonal Subspaces—0
Learning Discriminative Video Representations Using Adversarial Perturbations—0
Automatic differentiation for Riemannian optimization on low-rank matrix and tensor-train manifolds—0
Affinity guided Geometric Semi-Supervised Metric Learning—0
Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties—0
Learning Mixed-Curvature Representations in Product Spaces—0
Coupling Matrix Manifolds and Their Applications in Optimal Transport—0
A dual framework for low-rank tensor completion—0
Learning an Invariant Hilbert Space for Domain Adaptation—0
Design of Artificial Interference Signals for Covert Communication Aided by Multiple Friendly Nodes—0
Differential Evolution for Grassmann Manifold Optimization: A Projection Approach—0
Differentially private Riemannian optimization—0
Dimensionality Reduction on Grassmannian via Riemannian Optimization: A Generalized Perspective—0
A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints—0
Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform—0
Averaging Stochastic Gradient Descent on Riemannian Manifolds—0
Escape saddle points faster on manifolds via perturbed Riemannian stochastic recursive gradient—0
Understanding Riemannian Acceleration via a Proximal Extragradient Framework—0
Convergence and Complexity Guarantee for Inexact First-order Riemannian Optimization Algorithms—0
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation—0
Learning Discriminative ab-Divergences for Positive Definite Matrices—0
Communication-Efficient Distributed PCA by Riemannian Optimization—0
Fast Quantum Process Tomography via Riemannian Gradient Descent—0
Hamiltonian Monte-Carlo for Orthogonal Matrices—0
Fast Global Convergence for Low-rank Matrix Recovery via Riemannian Gradient Descent with Random Initialization—0
Approximate Birkhoff-von-Neumann decomposition: a differentiable approach—0
Gaussian-Mixture-Model Q-Functions for Reinforcement Learning by Riemannian Optimization—0
A Riemannian Optimization Perspective of the Gauss-Newton Method for Feedforward Neural Networks—0
Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization—0
Generalized Rank Pooling for Activity Recognition—0
Convergence results for projected line-search methods on varieties of low-rank matrices via Łojasiewicz inequality—0
Geometric optimisation on positive definite matrices for elliptically contoured distributions—0
Geometry Aware Constrained Optimization Techniques for Deep Learning—0
Higher Order Reduced Rank Regression—0
Faster Riemannian Newton-type Optimization by Subsampling and Cubic Regularization—0
Faster Randomized Methods for Orthogonality Constrained Problems—0
Leveraging Low-rank Factorizations of Conditional Correlation Matrices in Graph Learning—0
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