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Riemannian optimization
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Optimization methods on Riemannian manifolds.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AlexGrinch/ro_sgns
공식 코드https://github.com/Bihaqo/exp-machines
공식 코드https://github.com/GeometricBCI/Tensor-CSPNet-and-Graph-CSPNet
공식 코드https://github.com/IA3005/Elliptical-Wishart-for-Signal-Processing
공식 코드https://github.com/JasonJiaxiangLi/RADMM
공식 코드https://github.com/MelWe/rfw
공식 코드https://github.com/PouriaZ/GMML
공식 코드https://github.com/SPAICOM/calsep
공식 코드Causal Abstraction Learning based on the Semantic Embedding Principle
https://github.com/Yuan-Jinghui/Riemannian-Optimization-on-Relaxed-Indicator-Matrix-Manifold
공식 코드Riemannian Optimization on Relaxed Indicator Matrix Manifold
https://github.com/ahippert/graphfactormodel
공식 코드Learning Graphical Factor Models with Riemannian Optimization
https://github.com/andyjm3/AI-vs-BW
공식 코드On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry
https://github.com/andyjm3/RSDM
공식 코드Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method
https://github.com/andyjm3/blockspdot
공식 코드Riemannian block SPD coupling manifold and its application to optimal transport
https://github.com/andyjm3/gbw
공식 코드Learning with symmetric positive definite matrices via generalized Bures-Wasserstein geometry
https://github.com/antoinecollas/optim_compound
공식 코드Riemannian optimization for non-centered mixture of scaled Gaussian distributions
https://github.com/bghojogh/LBFGS-Vector-Transport-Free
공식 코드https://github.com/bokveizen/Feedback-Gradient-Descent
공식 코드Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNs
https://github.com/cjyaras/normalized-neural-collapse
공식 코드Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold
https://github.com/dnguyend/ManNullRange
공식 코드Operator-valued formulas for Riemannian Gradient and Hessian and families of tractable metrics
https://github.com/elizabethnewman/star-m-opt
공식 코드Optimal Matrix-Mimetic Tensor Algebras via Variable Projection
https://github.com/fedelopez77/sympa
공식 코드Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach
https://github.com/flgoyens/nonlinear-matrix-recovery
공식 코드Nonlinear matrix recovery using optimization on the Grassmann manifold
https://github.com/flgoyens/variety-registration
공식 코드Registration of algebraic varieties using Riemannian optimization
https://github.com/geomstats/geomstats
공식 코드geomstats: a Python Package for Riemannian Geometry in Machine Learning
https://github.com/geoopt/geoopt
공식 코드https://github.com/hiroyuki-kasai/RSOpt
공식 코드Riemannian stochastic variance reduced gradient on Grassmann manifold
https://github.com/jacobh140/century-of-college-football
공식 코드A Spectral Framework for Tracking Communities in Evolving Networks
https://github.com/jacobh140/spectral-dcd
공식 코드A Spectral Framework for Tracking Communities in Evolving Networks
https://github.com/jychen18/rpmg
공식 코드Projective Manifold Gradient Layer for Deep Rotation Regression
https://github.com/pchlenski/hyperdt
공식 코드https://github.com/pymanopt/pymanopt
공식 코드Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation
https://github.com/rezagodaz/accurate-partial-svd
공식 코드Accurate and fast matrix factorization for low-rank learning
https://github.com/rohitrango/fireants
공식 코드FireANTs: Adaptive Riemannian Optimization for Multi-Scale Diffeomorphic Matching
https://github.com/saitejautpala/rieoptax
공식 코드https://github.com/simonvary/landing-generalized-stiefel
공식 코드Optimization without Retraction on the Random Generalized Stiefel Manifold
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.