Tensorial Template

Tensorial templates represent data using multi-dimensional arrays (tensors), offering efficient ways to handle complex, high-dimensional information in various applications. Current research focuses on developing algorithms and model architectures that leverage tensor decompositions (like tensor train) to reduce computational complexity and memory footprint, particularly within deep learning and scientific computing. This approach improves the speed and efficiency of tasks such as template matching, tensor regression, and neural network training, impacting fields ranging from computer vision and tomography to natural language processing. The resulting improvements in efficiency and accuracy are significant for handling increasingly large and complex datasets.