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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 1–10 of 796 papers

TitleStatusHype
Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion AlgorithmsCode2
Crosslingual Topic Modeling with WikiPDACode1
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
AIR-Net: Adaptive and Implicit Regularization Neural Network for Matrix CompletionCode1
Adversarial Crowdsourcing Through Robust Rank-One Matrix CompletionCode1
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient DescentCode1
Causal Matrix CompletionCode1
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