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Low-Rank Matrix Completion
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Low-Rank Matrix Completion is an important problem with several applications in areas such as recommendation systems, sketching, and quantum tomography. The goal in matrix completion is to recover a low rank matrix, given a small number of entries of the matrix.
Source: Universal Matrix Completion
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/anikpram/Deep-SLR
공식 코드Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)
https://github.com/aradha/lin-rfm
공식 코드Linear Recursive Feature Machines provably recover low-rank matrices
https://github.com/cbig-iowa/giraf
공식 코드Structured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine Learning
https://github.com/cjyaras/deep-lora-transformers
공식 코드Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
https://github.com/ckuemmerle/MatrixIRLS
공식 코드Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method
https://github.com/heyang49/TW-LSMC
공식 코드https://github.com/hiroyuki-kasai/RSOpt
공식 코드Riemannian stochastic variance reduced gradient on Grassmann manifold
https://github.com/lee-ny/teaching_arithmetic
공식 코드https://github.com/mohit-shrma/matfac
공식 코드Adaptive Matrix Completion for the Users and the Items in Tail
https://github.com/pizilber/GNMR
공식 코드GNMR: A provable one-line algorithm for low rank matrix recovery
https://github.com/shahanaibrahimosu/mixed-membership-graph-clustering
공식 코드https://github.com/soominkwon/comp-deep-nets
공식 코드Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning Dynamics
https://github.com/tienmt/UQMC
공식 코드Simulation comparisons between Bayesian and de-biased estimators in low-rank matrix completion
https://github.com/vdaneshpajooh/NORST-rmc
공식 코드Provable Subspace Tracking from Missing Data and Matrix Completion
https://github.com/wanghailin97/Guaranteed-Tensor-Recovery-Fused-Low-rankness-and-Smoothness
공식 코드Guaranteed Tensor Recovery Fused Low-rankness and Smoothness
https://github.com/xuehy/depthInpainting
공식 코드Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient Regularization
https://github.com/HauLiang/Matrix-Completion-Methods
https://github.com/THweinberger/specAna_matComp
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method
https://github.com/gregongie/vmc
https://github.com/jasonsun0310/MatrixCompletion.jl
Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion
https://github.com/naist-nlp/mbrs
Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms
https://github.com/strin/pyOptSpace
A Gradient Descent Algorithm on the Grassman Manifold for Matrix Completion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.