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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 1–25 of 796 papers

TitleStatusHype
Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion AlgorithmsCode2
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & AdaptationCode1
Randomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image InpaintingCode1
Linear Recursive Feature Machines provably recover low-rank matricesCode1
Teaching Arithmetic to Small TransformersCode1
Hyperparameter optimization in deep multi-target predictionCode1
Adaptive and Implicit Regularization for Matrix CompletionCode1
Sensing Theorems for Unsupervised Learning in Linear Inverse ProblemsCode1
Indiscriminate Poisoning Attacks on Unsupervised Contrastive LearningCode1
Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A BenchmarkCode1
AIR-Net: Adaptive and Implicit Regularization Neural Network for Matrix CompletionCode1
Causal Matrix CompletionCode1
GLocal-K: Global and Local Kernels for Recommender SystemsCode1
Inductive Matrix Completion Using Graph AutoencoderCode1
Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural NetworksCode1
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few SamplesCode1
Scalable and Explainable 1-Bit Matrix Completion via Graph Signal LearningCode1
Deep Permutation Equivariant Structure from MotionCode1
Adversarial Crowdsourcing Through Robust Rank-One Matrix CompletionCode1
Crosslingual Topic Modeling with WikiPDACode1
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order MethodCode1
Compressed sensing of low-rank plus sparse matricesCode1
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering ApproachCode1
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian CopulaCode1
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient DescentCode1
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