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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 21–30 of 796 papers

TitleStatusHype
GLocal-K: Global and Local Kernels for Recommender SystemsCode1
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order MethodCode1
Graph Convolutional Matrix CompletionCode1
Hyperparameter optimization in deep multi-target predictionCode1
Adversarial Crowdsourcing Through Robust Rank-One Matrix CompletionCode1
Inductive Matrix Completion Based on Graph Neural NetworksCode1
k-Space Deep Learning for Accelerated MRICode1
Linear Recursive Feature Machines provably recover low-rank matricesCode1
Causal Matrix CompletionCode1
Sensing Theorems for Unsupervised Learning in Linear Inverse ProblemsCode1
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