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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 1–50 of 796 papers

TitleStatusHype
Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion AlgorithmsCode2
AIR-Net: Adaptive and Implicit Regularization Neural Network for Matrix CompletionCode1
Causal Matrix CompletionCode1
SAVOIAS: A Diverse, Multi-Category Visual Complexity DatasetCode1
Linear Recursive Feature Machines provably recover low-rank matricesCode1
Indiscriminate Poisoning Attacks on Unsupervised Contrastive LearningCode1
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
Geometric Matrix Completion with Recurrent Multi-Graph Neural NetworksCode1
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering ApproachCode1
Sensing Theorems for Unsupervised Learning in Linear Inverse ProblemsCode1
Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural NetworksCode1
Randomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image InpaintingCode1
k-Space Deep Learning for Accelerated MRICode1
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order MethodCode1
GLocal-K: Global and Local Kernels for Recommender SystemsCode1
Crosslingual Topic Modeling with WikiPDACode1
Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A BenchmarkCode1
Generalized Low Rank ModelsCode1
Hyperparameter optimization in deep multi-target predictionCode1
Inductive Matrix Completion Using Graph AutoencoderCode1
Matrix Completion and Low-Rank SVD via Fast Alternating Least SquaresCode1
OBOE: Collaborative Filtering for AutoML Model SelectionCode1
Teaching Arithmetic to Small TransformersCode1
Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)Code1
Adversarial Crowdsourcing Through Robust Rank-One Matrix CompletionCode1
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian CopulaCode1
Scalable and Explainable 1-Bit Matrix Completion via Graph Signal LearningCode1
Inductive Matrix Completion Based on Graph Neural NetworksCode1
Graph Convolutional Matrix CompletionCode1
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient DescentCode1
Adaptive and Implicit Regularization for Matrix CompletionCode1
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few SamplesCode1
Compressed sensing of low-rank plus sparse matricesCode1
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & AdaptationCode1
Deep Permutation Equivariant Structure from MotionCode1
A General Framework for Fast Stagewise Algorithms—0
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion—0
Active Feature Acquisition with Supervised Matrix Completion—0
A Fast Matrix-Completion-Based Approach for Recommendation Systems—0
Adversarial Robust Low Rank Matrix Estimation: Compressed Sensing and Matrix Completion—0
A Comparison of Clustering and Missing Data Methods for Health Sciences—0
Abrupt Learning in Transformers: A Case Study on Matrix Completion—0
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion—0
Low-rank matrix completion theory via Plucker coordinates—0
Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0—0
A More Stable Accelerated Gradient Method Inspired by Continuous-Time Perspective—0
A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion—0
Advancing Matrix Completion by Modeling Extra Structures beyond Low-Rankness—0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms—0
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