Tensor Code

Tensor codes are efficient methods for compressing and manipulating multi-dimensional data arrays (tensors), crucial for handling the massive datasets in machine learning and other fields. Current research focuses on developing novel algorithms, such as neural tensor train decomposition and adaptations of video codecs, to achieve high compression ratios while maintaining data fidelity, even without strong assumptions about the tensor's structure. These advancements are significant because they address the computational bottlenecks associated with large models, enabling faster training and inference on less powerful hardware and improving the efficiency of deep learning compilers.