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 161–170 of 796 papers

TitleStatusHype
Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks—0
Spectal Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning—0
Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction—0
Implicit bias of SGD in L_2-regularized linear DNNs: One-way jumps from high to low rank—0
Matrix Estimation for Offline Reinforcement Learning with Low-Rank Structure—0
Disjunctive Branch-And-Bound for Certifiably Optimal Low-Rank Matrix Completion—0
Matrix tri-factorization over the tropical semiringCode0
Rotation Synchronization via Deep Matrix FactorizationCode0
MISNN: Multiple Imputation via Semi-parametric Neural Networks—0
Quaternion Matrix Completion Using Untrained Quaternion Convolutional Neural Network for Color Image Inpainting—0
Show:102550
← PrevPage 17 of 80Next →

No leaderboard results yet.