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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

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Showing 31–40 of 796 papers

TitleStatusHype
Adaptive and Implicit Regularization for Matrix CompletionCode1
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few SamplesCode1
Compressed sensing of low-rank plus sparse matricesCode1
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & AdaptationCode1
Deep Permutation Equivariant Structure from MotionCode1
A General Framework for Fast Stagewise Algorithms—0
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion—0
Active Feature Acquisition with Supervised Matrix Completion—0
A Fast Matrix-Completion-Based Approach for Recommendation Systems—0
Adversarial Robust Low Rank Matrix Estimation: Compressed Sensing and Matrix Completion—0
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