Tensor completion methods based on the tensor train (TT) have the issues of inaccurate weight assignment and ineffective tensor augmentation pre-processing. In this work, we propose a novel tensor ...

Besides, we extend the FP16 Tensor-Cores-based QR factorization to accommodate FP32 and FP64 on FP16 and INT8 Tensor Cores, respectively. Additionally, to address the issue of orthogonality loss in ...