GitHub

A Python demonstration of speeding up matrix multiplication and reducing memory using a quantum-inspired low-rank SVD trick, with examples in AI.

This repository demonstrates a powerful, classical linear algebra technique—low-rank approximation via Singular Value Decomposition (SVD)—to dramatically accelerate common matrix operations like GEMM ...
GitHub

Matrix-based Rank Adaptation (MoRA) in Python.md

Matrix-based Rank Adaptation (MoRA) is a technique used in information retrieval (IR) to improve the ranking of search results by incorporating term-term similarity information. It aims to enhance the ...
Frontiers

Independent Low-Rank Matrix Analysis-Based Automatic Artifact Reduction Technique Applied to Three BCI Paradigms

1 Artificial Intelligence Research Center, Department of Information Technology and Human Factors, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan 2 Graduate ...
IEEE

Semi-Supervised Symmetric Non-Negative Matrix Factorization With Low-Rank Tensor Representation

Abstract: Semi-supervised symmetric non-negative matrix factorization (SNMF) utilizes the available supervisory information (usually in the form of pairwise constraints) to improve the clustering ...
IEEE

Low-Rank Matrix Recovery with Total Generalized Variation for Defending Adversarial Examples

Abstract: Low-rank matrix decomposition with first-order total variation (TV) regularization exhibits excellent performance in exploration of image structure. Taking advantage of its excellent ...