Extending Tensor Depth Efficiency to Tensor-Train and RNN-like Models

This project extended a classic tensor-analysis paper on deep learning expressivity by replacing the original CP/HT setup with a tensor-train decomposition, connecting the result to an RNN-like architecture and deriving an analogous exponential rank lower bound.