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 91–100 of 796 papers

TitleStatusHype
Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models—0
Doubly Robust Inference in Causal Latent Factor Models—0
Convergence of Gradient Descent with Small Initialization for Unregularized Matrix Completion—0
High Dimensional Factor Analysis with Weak Factors—0
Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness—0
Data-driven model selection within the matrix completion method for causal panel data models—0
Fast Dual-Regularized Autoencoder for Sparse Biological Data—0
On the Robustness of Cross-Concentrated Sampling for Matrix Completion—0
Matrix Completion with Hypergraphs:Sharp Thresholds and Efficient Algorithms—0
Effect of Beampattern on Matrix Completion with Sparse Arrays—0
Show:102550
← PrevPage 10 of 80Next →

No leaderboard results yet.