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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 71–80 of 796 papers

TitleStatusHype
Multi-Channel Hypergraph Contrastive Learning for Matrix Completion—0
Abrupt Learning in Transformers: A Case Study on Matrix Completion—0
Bayesian Collaborative Bandits with Thompson Sampling for Improved Outreach in Maternal Health Program—0
Low-rank Bayesian matrix completion via geodesic Hamiltonian Monte Carlo on Stiefel manifolds—0
Learning Counterfactual Distributions via Kernel Nearest Neighbors—0
Distributional Matrix Completion via Nearest Neighbors in the Wasserstein Space—0
Can We Predict Performance of Large Models across Vision-Language Tasks?Code0
Riemannian Optimization for Non-convex Euclidean Distance Geometry with Global Recovery Guarantees—0
Hierarchical Matrix Completion for the Prediction of Properties of Binary Mixtures—0
Tailed Low-Rank Matrix Factorization for Similarity Matrix Completion—0
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