We propose GLassoformer, a novel and efficient transformer architecture leveraging group Lasso regularization to reduce the number of queries of the standard self-attention mechanism. Due to the sparsified queries, GLassoformer is more computationally efficient than the standard transformers. On the power grid post-fault voltage prediction task, GLassoformer shows remarkably better prediction than many existing benchmark algorithms in terms of accuracy and stability.
@article{arxiv.2201.09145,
title = {glassoformer: a query-sparse transformer for post-fault power grid voltage prediction},
author = {Yunling Zheng and Carson Hu and Guang Lin and Meng Yue and Bao Wang and Jack Xin},
journal= {arXiv preprint arXiv:2201.09145},
year = {2022}
}