3 papers
cs.LG2026
Addition is almost all you need: Compressing large language models with double binary factorization
VladimÃr Boža, VladimÃr Macko
Binary quantization approaches, which replace weight matrices with binary matrices and substitute costly multiplications with cheaper additions, offer a computationally efficient a…
cs.LG2025
MACKO: Sparse Matrix-Vector Multiplication for Low Sparsity
VladimÃr Macko, VladimÃr Boža
Sparse Matrix-Vector Multiplication (SpMV) is a fundamental operation in the inference of sparse Large Language Models (LLMs). Because existing SpMV methods perform poorly under th…
cs.LG2025
Two Sparse Matrices are Better than One: Sparsifying Neural Networks with Double Sparse Factorization
VladimÃr Boža, VladimÃr Macko
Neural networks are often challenging to work with due to their large size and complexity. To address this, various methods aim to reduce model size by sparsifying or decomposing w…