Matrix Factorization / Decomposition
8 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Matrix Factorization / Decomposition
Most implemented papers
Joint Matrix-Tensor Factorization for Knowledge Base Inference
dair-iitd/kbi
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If not, what characteristics of a dataset determine the performance of MF and TF models?
NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access
alexrenz/NuPS
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Parameter servers (PSs) facilitate the implementation of distributed training for large machine learning tasks.
Eigenvalue and Generalized Eigenvalue Problems: Tutorial
This paper is a tutorial for eigenvalue and generalized eigenvalue problems.
Instance Ranking and Numerosity Reduction Using Matrix Decomposition and Subspace Learning
Using this similarity measure, we propose several related algorithms for ranking data instances and performing numerosity reduction.
Fast Rank Reduction for Non-negative Matrices via Mean Field Theory
We propose an efficient matrix rank reduction method for non-negative matrices, whose time complexity is quadratic in the number of rows or columns of a matrix.
Accurate and fast matrix factorization for low-rank learning
In this paper, we tackle two important problems in low-rank learning, which are partial singular value decomposition and numerical rank estimation of huge matrices.
Joint Matrix Decomposition for Deep Convolutional Neural Networks Compression
To overcome this problem, we propose to compress CNNs and alleviate performance degradation via joint matrix decomposition, which is different from existing works that compressed layers separately.
Fast Rank-1 NMF for Missing Data with KL Divergence
We propose a fast non-gradient-based method of rank-1 non-negative matrix factorization (NMF) for missing data, called A1GM, that minimizes the KL divergence from an input matrix to the reconstructed rank-1 matrix.
