Tensor Train Decomposition

Tensor Train (TT) decomposition is a low-rank tensor approximation method aiming to efficiently represent and manipulate high-dimensional data by reducing storage and computational costs. Current research focuses on applying TT decomposition to compress various machine learning models, including neural networks (both standard and spiking) and transformers, as well as improving algorithms for TT-based regression and optimization. This technique offers significant advantages in resource-constrained environments like edge computing and enables the analysis of large-scale datasets that would otherwise be intractable, impacting fields ranging from image processing and natural language processing to scientific computing.