Tensor Compression

Tensor compression aims to reduce the storage and computational demands of high-dimensional data, crucial for handling large datasets in machine learning and scientific computing. Current research focuses on developing efficient algorithms, including those based on tensor decompositions (like Tensor Train and Tucker), and leveraging specialized hardware like video codecs for compression. These advancements are significantly impacting fields like deep learning by accelerating training and inference of large models, enabling the use of larger datasets and more complex models on resource-constrained systems.