SOTAVerified

Low-Rank Matrix Completion

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 158 papers

TitleStatusHype
Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion AlgorithmsCode2
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & AdaptationCode1
Randomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image InpaintingCode1
Linear Recursive Feature Machines provably recover low-rank matricesCode1
Teaching Arithmetic to Small TransformersCode1
Guaranteed Tensor Recovery Fused Low-rankness and SmoothnessCode1
Generalized Nonconvex Approach for Low-Tubal-Rank Tensor RecoveryCode1
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few SamplesCode1
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order MethodCode1
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
Show:102550
← PrevPage 1 of 16Next →

No leaderboard results yet.