Matrix Factorization / Decomposition

8 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Joint Matrix-Tensor Factorization for Knowledge Base Inference

dair-iitd/kbi 2 Jun 2017

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 1 Apr 2021

Parameter servers (PSs) facilitate the implementation of distributed training for large machine learning tasks.

Eigenvalue and Generalized Eigenvalue Problems: Tutorial

bghojogh/Generalized-Eigenvalue-Problem 25 Mar 2019

This paper is a tutorial for eigenvalue and generalized eigenvalue problems.

Fast Rank Reduction for Non-negative Matrices via Mean Field Theory

gkazunii/Legendre-tucker-rank-reduction 9 Jun 2020

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

rezagodaz/accurate-partial-svd 21 Apr 2021

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

ShaowuChen/JointSVD 9 Jul 2021

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

gkazunii/A1GM 25 Oct 2021

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.