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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

Title	Date	Tasks	Status	Hype	Score
Inductive Matrix Completion Using Graph Autoencoder	Aug 25, 2021	Graph Neural NetworkMatrix Completion	CodeCode Available	1	5
Deep Permutation Equivariant Structure from Motion	Apr 14, 2021	Matrix Completion	CodeCode Available	1	5
OBOE: Collaborative Filtering for AutoML Model Selection	Aug 9, 2018	Active LearningAutoML	CodeCode Available	1	5
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method	Sep 7, 2020	Low-Rank Matrix CompletionMatrix Completion	CodeCode Available	1	5
GLocal-K: Global and Local Kernels for Recommender Systems	Aug 27, 2021	Collaborative FilteringMatrix Completion	CodeCode Available	1	5
Counterfactual inference for sequential experiments	Feb 14, 2022	counterfactualCounterfactual Inference	CodeCode Available	0	5
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset	Nov 4, 2015	Matrix Completion	CodeCode Available	0	5
Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power Injections	Dec 2, 2022	Matrix Completion	CodeCode Available	0	5
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation	Jun 12, 2025	Contrastive LearningDenoising	CodeCode Available	0	5
Deep Collective Matrix Factorization for Augmented Multi-View Learning	Nov 28, 2018	Bayesian OptimizationMatrix Completion	CodeCode Available	0	5

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