On the Expressive Power of Behavior Structure
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
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| _version_ | 1866917556810416128 |
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| author | Wang, Cheng Zhu, Hangyu Lin, Yuhang Jiang, Changjun |
| author_facet | Wang, Cheng Zhu, Hangyu Lin, Yuhang Jiang, Changjun |
| contents | Efforts toward a comprehensive description of behavior have indeed facilitated the development of representation-based approaches that utilize deep learning to capture behavioral information. As behavior complexity increases, the expressive power of these models reaches a bottleneck. We coin the term ``behavioral molecular structure" and propose a new model called the Behavioral Molecular Structure (BMS). The model characterizes behaviors at the atomic level, analogizes behavioral attributes to atoms, and concretizes interrelations at the granularity of atoms using graphs. Here, we design three different downstream tasks to test the performance of the BMS model on public datasets. Additionally, we provide a preliminary theoretical analysis demonstrating that the BMS can offer effective expressiveness for complex behaviors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_01652 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | On the Expressive Power of Behavior Structure Wang, Cheng Zhu, Hangyu Lin, Yuhang Jiang, Changjun Computational Engineering, Finance, and Science Efforts toward a comprehensive description of behavior have indeed facilitated the development of representation-based approaches that utilize deep learning to capture behavioral information. As behavior complexity increases, the expressive power of these models reaches a bottleneck. We coin the term ``behavioral molecular structure" and propose a new model called the Behavioral Molecular Structure (BMS). The model characterizes behaviors at the atomic level, analogizes behavioral attributes to atoms, and concretizes interrelations at the granularity of atoms using graphs. Here, we design three different downstream tasks to test the performance of the BMS model on public datasets. Additionally, we provide a preliminary theoretical analysis demonstrating that the BMS can offer effective expressiveness for complex behaviors. |
| title | On the Expressive Power of Behavior Structure |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2312.01652 |