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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 61–70 of 796 papers

TitleStatusHype
Learning Iterative Reasoning through Energy Diffusion—0
Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical PropertiesCode0
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data—0
Symmetric Matrix Completion with ReLU Sampling—0
Structured Learning of Compositional Sequential InterventionsCode0
Convergence of the majorized PAM method with subspace correction for low-rank composite factorization model—0
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & AdaptationCode1
Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion AlgorithmsCode2
Matrix Low-Rank Trust Region Policy OptimizationCode0
Matrix Low-Rank Approximation For Policy Gradient MethodsCode0
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