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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
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & AdaptationCode1
Randomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image InpaintingCode1
Linear Recursive Feature Machines provably recover low-rank matricesCode1
Teaching Arithmetic to Small TransformersCode1
Hyperparameter optimization in deep multi-target predictionCode1
Adaptive and Implicit Regularization for Matrix CompletionCode1
Sensing Theorems for Unsupervised Learning in Linear Inverse ProblemsCode1
Indiscriminate Poisoning Attacks on Unsupervised Contrastive LearningCode1
Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A BenchmarkCode1
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