| 研究実績の概要 |
In this fiscal year, we further study tensor completion algorithms and developed several novel approach such as Bayesian tensor completion algorithm and fully connected tensor network algorithm. In addition, we also study an open problem of tensor network structure search. Finally, several applications to multi-task learning and hyper spectral image processing have been conducted by applying tensor methods.
The transformer is widely studied and demonstrated to be a powerful architecture. However, it is difficult to be adopted into multi-modal learning, we have applied tensor representation for attention mechanism and developed multi-way multi-modal transformer, which is more efficient and powerful for multimodal learning. One practical variant of tensor network structure search, dubbed TN permutation search (TN-PS), in which we search for good mappings from tensor modes onto TN vertices (core tensors) for compact TN representations. We conduct a theoretical investigation of TN-PS and propose a practically-efficient algorithm to resolve the problem. We propose a unified paradigm combining the spatial and spectral properties for HSI restoration. The proposed paradigm enjoys performance superiority from the non-local spatial denoising and light computation complexity from the low-rank orthogonal basis exploration.
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