Technologies on Effectiveness and Efficiency: A Survey of State Spaces Models
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929759818088448 |
|---|---|
| author | Lv, Xingtai Sun, Youbang Zhang, Kaiyan Qu, Shang Zhu, Xuekai Fan, Yuchen Wu, Yi Hua, Ermo Long, Xinwei Ding, Ning Zhou, Bowen |
| author_facet | Lv, Xingtai Sun, Youbang Zhang, Kaiyan Qu, Shang Zhu, Xuekai Fan, Yuchen Wu, Yi Hua, Ermo Long, Xinwei Ding, Ning Zhou, Bowen |
| contents | State Space Models (SSMs) have emerged as a promising alternative to the popular transformer-based models and have been increasingly gaining attention. Compared to transformers, SSMs excel at tasks with sequential data or longer contexts, demonstrating comparable performances with significant efficiency gains. In this survey, we provide a coherent and systematic overview for SSMs, including their theoretical motivations, mathematical formulations, comparison with existing model classes, and various applications. We divide the SSM series into three main sections, providing a detailed introduction to the original SSM, the structured SSM represented by S4, and the selective SSM typified by Mamba. We put an emphasis on technicality, and highlight the various key techniques introduced to address the effectiveness and efficiency of SSMs. We hope this manuscript serves as an introduction for researchers to explore the theoretical foundations of SSMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11224 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Technologies on Effectiveness and Efficiency: A Survey of State Spaces Models Lv, Xingtai Sun, Youbang Zhang, Kaiyan Qu, Shang Zhu, Xuekai Fan, Yuchen Wu, Yi Hua, Ermo Long, Xinwei Ding, Ning Zhou, Bowen Machine Learning Artificial Intelligence Computation and Language State Space Models (SSMs) have emerged as a promising alternative to the popular transformer-based models and have been increasingly gaining attention. Compared to transformers, SSMs excel at tasks with sequential data or longer contexts, demonstrating comparable performances with significant efficiency gains. In this survey, we provide a coherent and systematic overview for SSMs, including their theoretical motivations, mathematical formulations, comparison with existing model classes, and various applications. We divide the SSM series into three main sections, providing a detailed introduction to the original SSM, the structured SSM represented by S4, and the selective SSM typified by Mamba. We put an emphasis on technicality, and highlight the various key techniques introduced to address the effectiveness and efficiency of SSMs. We hope this manuscript serves as an introduction for researchers to explore the theoretical foundations of SSMs. |
| title | Technologies on Effectiveness and Efficiency: A Survey of State Spaces Models |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2503.11224 |