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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 2650 of 796 papers

TitleStatusHype
Crosslingual Topic Modeling with WikiPDACode1
Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A BenchmarkCode1
Causal Matrix CompletionCode1
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient DescentCode1
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
Adaptive and Implicit Regularization for Matrix CompletionCode1
Generalized Low Rank ModelsCode1
GLocal-K: Global and Local Kernels for Recommender SystemsCode1
Graph Convolutional Matrix CompletionCode1
Inductive Matrix Completion Based on Graph Neural NetworksCode1
Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory DataCode0
Efficient Model-Based Collaborative Filtering with Fast Adaptive PCACode0
Dictionary Learning for Massive Matrix FactorizationCode0
Distant Supervision for Relation Extraction with Matrix CompletionCode0
Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient DescentCode0
Adversarially-Trained Nonnegative Matrix FactorizationCode0
Graphon Estimation from Partially Observed Network DataCode0
Deep Models of Interactions Across SetsCode0
Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix CompletionCode0
Spectral Geometric Matrix CompletionCode0
DeepVir -- Graphical Deep Matrix Factorization for "In Silico" Antiviral Repositioning: Application to COVID-19Code0
Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient RegularizationCode0
Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power InjectionsCode0
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust RecommendationCode0
Collective Matrix CompletionCode0
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