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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 691–700 of 796 papers

TitleStatusHype
Algebraic Variety Models for High-Rank Matrix CompletionCode0
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral FiltersCode0
Deep Models of Interactions Across SetsCode0
GNMR: A provable one-line algorithm for low rank matrix recoveryCode0
Deep Collective Matrix Factorization for Augmented Multi-View LearningCode0
DeepVir -- Graphical Deep Matrix Factorization for "In Silico" Antiviral Repositioning: Application to COVID-19Code0
NGS Based Haplotype Assembly Using Matrix CompletionCode0
State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual PredictionCode0
Policy Augmentation: An Exploration Strategy for Faster Convergence of Deep Reinforcement Learning AlgorithmsCode0
Low-Rank Hankel Tensor Completion for Traffic Speed EstimationCode0
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