Papers with Code 아카이브

Riemannian optimization

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/simonvary/landing-stiefel

공식 코드

Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints

https://github.com/stat-ml/TTDE

공식 코드

Tensor-Train Density Estimation

https://github.com/trustinai/gdvaecode

공식 코드

The Gaussian Discriminant Variational Autoencoder (GdVAE): A Self-Explainable Model with Counterfactual Explanations

https://github.com/utvisionlab/mixest

공식 코드

MixEst: An Estimation Toolbox for Mixture Models

https://github.com/yanhaojin/generalized-ridge-regression-for-meta-learning

공식 코드

Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation

https://github.com/yihuazhu111/orthogonale

공식 코드

Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding

https://github.com/yogeshd-iitk/subspace_descent_over_SPD_manifold

공식 코드

Low-complexity subspace-descent over symmetric positive definite manifold

https://github.com/yumeng5/Spherical-Text-Embedding

공식 코드

Spherical Text Embedding

https://github.com/FranxYao/PoincareProbe

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/HazyResearch/hgcn

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/MIT-SPARK/FUSES

Accelerated Inference in Markov Random Fields via Smooth Riemannian Optimization

https://github.com/TatsuyaShirakawa/poincare-embedding

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/adsieg/Multi_Text_Classification

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/dalab/hyperbolic_cones

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/drewwilimitis/hyperbolic-learning

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/drewwilimitis/poincare

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/emstoudenmire/TNML

Exponential Machines

https://github.com/facebookresearch/poincare-embeddings

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/jemisjoky/torchmps

Exponential Machines

https://github.com/ninamiolane/geomstats

geomstats: a Python Package for Riemannian Geometry in Machine Learning

https://github.com/nishnik/poincare_embeddings

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/nlpAThits/hyfi

Geoopt: Riemannian Optimization in PyTorch

https://github.com/rballester/ttrecipes

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

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.