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State Space Model for New-Generation Network Alternative to Transformers: A Survey
April 16, 2024, 4:42 a.m. | Xiao Wang, Shiao Wang, Yuhe Ding, Yuehang Li, Wentao Wu, Yao Rong, Weizhe Kong, Ju Huang, Shihao Li, Haoxiang Yang, Ziwen Wang, Bo Jiang, Chenglong Li
cs.LG updates on arXiv.org arxiv.org
Abstract: In the post-deep learning era, the Transformer architecture has demonstrated its powerful performance across pre-trained big models and various downstream tasks. However, the enormous computational demands of this architecture have deterred many researchers. To further reduce the complexity of attention models, numerous efforts have been made to design more efficient methods. Among them, the State Space Model (SSM), as a possible replacement for the self-attention based Transformer model, has drawn more and more attention in …
arxiv cs.ai cs.cl cs.cv cs.lg cs.mm network space state state space model survey transformers type
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