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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 11–20 of 796 papers

TitleStatusHype
AIR-Net: Adaptive and Implicit Regularization Neural Network for Matrix CompletionCode1
Causal Matrix CompletionCode1
GLocal-K: Global and Local Kernels for Recommender SystemsCode1
Inductive Matrix Completion Using Graph AutoencoderCode1
Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural NetworksCode1
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few SamplesCode1
Scalable and Explainable 1-Bit Matrix Completion via Graph Signal LearningCode1
Deep Permutation Equivariant Structure from MotionCode1
Adversarial Crowdsourcing Through Robust Rank-One Matrix CompletionCode1
Crosslingual Topic Modeling with WikiPDACode1
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