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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

Showing 126150 of 796 papers

TitleStatusHype
State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual PredictionCode0
Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient RegularizationCode0
Distant Supervision for Relation Extraction with Matrix CompletionCode0
Spectral Geometric Matrix CompletionCode0
Collaborative Filtering with Graph Information: Consistency and Scalable MethodsCode0
Deep Models of Interactions Across SetsCode0
Deep Collective Matrix Factorization for Augmented Multi-View LearningCode0
A Generalized Latent Factor Model Approach to Mixed-data Matrix Completion with Entrywise ConsistencyCode0
Counterfactual inference for sequential experimentsCode0
Adaptive Matrix Completion for the Users and the Items in TailCode0
Adaptively-weighted Nearest Neighbors for Matrix CompletionCode0
A Perturbation Bound on the Subspace Estimator from Canonical ProjectionsCode0
Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power InjectionsCode0
A Gradient Descent Algorithm on the Grassman Manifold for Matrix CompletionCode0
Distributional Matrix Completion via Nearest Neighbors in the Wasserstein SpaceCode0
Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory DataCode0
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust RecommendationCode0
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes FlowCode0
Faster Matrix Completion Using Randomized SVDCode0
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale DatasetCode0
DeepVir -- Graphical Deep Matrix Factorization for "In Silico" Antiviral Repositioning: Application to COVID-19Code0
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural NetworksCode0
Implicit Regularization in Deep Matrix FactorizationCode0
Matrix Norm Estimation from a Few EntriesCode0
Training Complex Models with Multi-Task Weak SupervisionCode0
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