Low-Rank Matrix Completion
25 papers with code • 0 benchmarks • 0 datasets
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
Benchmarks
These leaderboards are used to track progress in Low-Rank Matrix Completion
Most implemented papers
Guaranteed Tensor Recovery Fused Low-rankness and Smoothness
Recent research have made significant progress by adopting two insightful tensor priors, i. e., global low-rankness (L) and local smoothness (S) across different tensor modes, which are always encoded as a sum of two separate regularization terms into the recovery models.
Teaching Arithmetic to Small Transformers
lee-ny/teaching_arithmetic
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Even in the complete absence of pretraining, this approach significantly and simultaneously improves accuracy, sample complexity, and convergence speed.
Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning Dynamics
We empirically evaluate the effectiveness of our compression technique on matrix recovery problems.
Linear Recursive Feature Machines provably recover low-rank matrices
aradha/lin-rfm
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A possible explanation is that common training algorithms for neural networks implicitly perform dimensionality reduction - a process called feature learning.
Matrix Completion with Convex Optimization and Column Subset Selection
ZAL-NASK/CSMC
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We present two algorithms that implement our Columns Selected Matrix Completion (CSMC) method, each dedicated to a different size problem.
