Summary of State Space Model For New-generation Network Alternative to Transformers: a Survey, by Xiao Wang et al.
State Space Model for New-Generation Network Alternative to Transformers: A Survey
by 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, Yaowei Wang, Yonghong Tian, Jin Tang
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Transformer architecture has shown impressive performance across various tasks, but its high computational demands have hindered further research. To address this issue, the State Space Model (SSM) has gained attention as a potential replacement for self-attention based Transformers. This paper provides a comprehensive review of SSMs and their applications in natural language processing, computer vision, graph, multi-modal, point cloud/event stream, time series data, and other domains. The authors describe the principles behind SSMs, review existing models, and provide statistical comparisons to demonstrate their effectiveness on various tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper reviews the State Space Model (SSM) as a possible replacement for the Transformer architecture. This model has gained attention due to its lower computational demands. The authors look at how SSMs have been used in different areas such as natural language processing, computer vision, and more. They also compare the performance of different SSM models on various tasks. |
Keywords
» Artificial intelligence » Attention » Multi modal » Natural language processing » Self attention » Time series » Transformer
