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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

Showing 41–50 of 796 papers

TitleStatusHype
Adversarially-Trained Nonnegative Matrix FactorizationCode0
Graphon Estimation from Partially Observed Network DataCode0
Deep Models of Interactions Across SetsCode0
Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix CompletionCode0
Spectral Geometric Matrix CompletionCode0
DeepVir -- Graphical Deep Matrix Factorization for "In Silico" Antiviral Repositioning: Application to COVID-19Code0
Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient RegularizationCode0
Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power InjectionsCode0
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust RecommendationCode0
Collective Matrix CompletionCode0
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