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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 51–75 of 796 papers

TitleStatusHype
Leave-One-Out Analysis for Nonconvex Robust Matrix Completion with General Thresholding Functions—0
Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0—0
Predictive Low Rank Matrix Learning under Partial Observations: Mixed-Projection ADMMCode0
Generalized Low-Rank Matrix Completion Model with Overlapping Group Error Representation—0
The heterogeneous impact of the EU-Canada agreement with causal machine learning—0
Optimized Waveform Design for OFDM-based ISAC Systems Under Limited Resource Occupancy—0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
Proximal Interacting Particle Langevin AlgorithmsCode0
Demystifying Language Model Forgetting with Low-rank Example Associations—0
Learning Translations via Matrix Completion—0
Learning Iterative Reasoning through Energy Diffusion—0
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data—0
Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical PropertiesCode0
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 Approximation For Policy Gradient MethodsCode0
Matrix Low-Rank Trust Region Policy OptimizationCode0
Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion—0
The radius of statistical efficiency—0
Subspace-Informed Matrix Completion—0
Efficient Federated Low Rank Matrix Completion—0
Discrete Aware Matrix Completion via Convexized _0-Norm Approximation—0
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