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 ...
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

Enhanced Low-Rank Matrix Decomposition for High-Resolution UAV-SAR Imagery

Abstract: Low-rank matrix decomposition is effective for sparse recovery. However, the conventions are limited in accuracy for high-resolution synthetic aperture radar (SAR) imagery due to the ...
thetechedvocate.org

How to calculate rank of a matrix

Rank is an essential concept in linear algebra that represents the number of linearly independent rows or columns of a matrix. It plays a crucial role in solving linear equations, determining the null ...
unite

LoRa, QLoRA and QA-LoRA: Efficient Adaptability in Large Language Models Through Low-Rank Matrix Factorization

Large Language Models (LLMs) have carved a unique niche, offering unparalleled capabilities in understanding and generating human-like text. The power of LLMs can be traced back to their enormous size ...