SOTAVerified

Matrix Completion

Matrix Completion is a method for recovering lost information. It originates from machine learning and usually deals with highly sparse matrices. Missing or unknown data is estimated using the low-rank matrix of the known data.

Source: A Fast Matrix-Completion-Based Approach for Recommendation Systems

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 381–390 of 796 papers

TitleStatusHype
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
Computational Graph Completion—0
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems—0
Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation—0
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization—0
Low-rank Bayesian matrix completion via geodesic Hamiltonian Monte Carlo on Stiefel manifolds—0
Computational Efficient Informative Nonignorable Matrix Completion: A Row- and Column-Wise Matrix U-Statistic Pseudo-Likelihood Approach—0
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery—0
Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction—0
Geometric Matrix Completion with Deep Conditional Random Fields—0
Show:102550
← PrevPage 39 of 80Next →

No leaderboard results yet.