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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 91–100 of 796 papers

TitleStatusHype
Amplify Graph Learning for Recommendation via Sparsity Completion—0
Optimized Waveform Design for OFDM-based ISAC Systems Under Limited Resource Occupancy—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
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