Tensor Data

Tensor data, representing multi-dimensional information, is increasingly central to scientific computing and machine learning. Current research focuses on efficient algorithms for tensor decomposition (e.g., Tucker, CP, Tensor Train), handling large-scale and sparse tensors, and developing robust methods for tensor completion and recovery in the presence of noise or missing data. These advancements are crucial for improving the scalability and performance of deep learning models, enabling efficient analysis of complex datasets in diverse fields like scientific imaging, graph neural networks, and signal processing, ultimately leading to more powerful and insightful data analysis.