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 51–60 of 158 papers

TitleStatusHype
Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction—0
Uncertainty Quantification For Low-Rank Matrix Completion With Heterogeneous and Sub-Exponential Noise—0
Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders—0
Weighted Low Rank Matrix Approximation and Acceleration—0
GNMR: A provable one-line algorithm for low rank matrix recoveryCode0
Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey—0
A Pre-training Oracle for Predicting Distances in Social Networks—0
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few SamplesCode1
Deep learned SVT: Unrolling singular value thresholding to obtain better MSE—0
Simulation comparisons between Bayesian and de-biased estimators in low-rank matrix completionCode0
Show:102550
← PrevPage 6 of 16Next →

No leaderboard results yet.