Tensor Train

Tensor Trains (TTs) are a low-rank tensor decomposition method aiming to efficiently represent and manipulate high-dimensional data by exploiting underlying structure. Current research focuses on applying TTs to improve the efficiency and scalability of large language models, neural networks, and other machine learning tasks, often incorporating them into novel architectures like Block Tensor-Train Mixture-of-Experts (BTT-MoE) or using them for parameter-efficient fine-tuning (e.g., TT-LoRA). This approach offers significant potential for reducing computational costs and memory requirements in various applications, from anomaly detection and image processing to solving high-dimensional partial differential equations and accelerating large-scale machine learning training.