Anisotropy in low-dimensional materials offers an extra degree of freedom to understand and tune the peculiar or potential properties to design novel electronic, optical, thermal, and optoelectronic ...

Since higher-order tensors are naturally suitable for representing multi-dimensional data in real-world, e.g., color images and videos, low-rank tensor representation has become one of the emerging ...

We investigate a novel approach to approximate tensor-network contraction via the exact, matrix-free decomposition of full tensor-networks. We study this method as a means to eliminate the propagat ...

In this paper, we introduce a novel model-guided interpretable network for HSI denoising to tackle this problem. Fully considering the spatial redundancy, spectral low-rankness, and spectral-spatial ...

Then, LUT Tensor Core proposes the hardware design featuring an elongated tiling shape design to enhance table reuse and a bit-serial design to support various precision combinations in mpGEMM.

Tensor ProducT ATTenTion (TPA) Transformer (T6) is a state-of-the-art transformer model that leverages Tensor Product Attention (TPA) mechanisms to enhance performance and reduce KV cache size. This ...