SOTAVerified|Agents Browse Leaderboard About

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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 711–720 of 796 papers

Title	Date	Tasks	Status
Guaranteed Rank Minimization via Singular Value Projection	Sep 30, 2009	Low-Rank Matrix CompletionMatrix Completion	CodeCode Available
On Low-rank Trace Regression under General Sampling Distribution	Apr 18, 2019	Matrix CompletionMulti-Task Learning	CodeCode Available
Distant Supervision for Relation Extraction with Matrix Completion	Jun 1, 2014	Low-Rank Matrix CompletionMatrix Completion	CodeCode Available
VIGAN: Missing View Imputation with Generative Adversarial Networks	Aug 22, 2017	DenoisingImputation	CodeCode Available
A Generalized Latent Factor Model Approach to Mixed-data Matrix Completion with Entrywise Consistency	Nov 17, 2022	Collaborative FilteringMatrix Completion	CodeCode Available
Adversarially-Trained Nonnegative Matrix Factorization	Apr 10, 2021	Dimensionality ReductionMatrix Completion	CodeCode Available
Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion	Aug 24, 2022	Collaborative FilteringMatrix Completion	CodeCode Available
Scalable Probabilistic Matrix Factorization with Graph-Based Priors	Aug 25, 2019	Matrix CompletionRecommendation Systems	CodeCode Available
Matrix Low-Rank Approximation For Policy Gradient Methods	May 27, 2024	Matrix CompletionPolicy Gradient Methods	CodeCode Available
Provable Tensor Ring Completion	Mar 8, 2019	Matrix Completion	CodeCode Available

Show:10 25 50

← PrevPage 72 of 80Next →

No leaderboard results yet.