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Low-Rank Matrix Completion

Low-Rank Matrix Completion is an important problem with several applications in areas such as recommendation systems, sketching, and quantum tomography. The goal in matrix completion is to recover a low rank matrix, given a small number of entries of the matrix.

Source: Universal Matrix Completion

Papers

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Showing 31–40 of 158 papers

TitleStatusHype
Teaching Arithmetic to Small TransformersCode1
Non-Convex Optimizations for Machine Learning with Theoretical Guarantee: Robust Matrix Completion and Neural Network Learning—0
Graph-Based Matrix Completion Applied to Weather Data—0
Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation—0
Matrix Completion from General Deterministic Sampling Patterns—0
Spectal Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning—0
Disjunctive Branch-And-Bound for Certifiably Optimal Low-Rank Matrix Completion—0
A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion—0
Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time—0
Guaranteed Tensor Recovery Fused Low-rankness and SmoothnessCode1
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