CW-CNN & CW-AN: Convolutional Networks and Attention Networks for CW-Complexes
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914938525581312 |
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| author | Khorana, Rahul |
| author_facet | Khorana, Rahul |
| contents | We present a novel framework for learning on CW-complex structured data points. Recent advances have discussed CW-complexes as ideal learning representations for problems in cheminformatics. However, there is a lack of available machine learning methods suitable for learning on CW-complexes. In this paper we develop notions of convolution and attention that are well defined for CW-complexes. These notions enable us to create the first Hodge informed neural network that can receive a CW-complex as input. We illustrate and interpret this framework in the context of supervised prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16686 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CW-CNN & CW-AN: Convolutional Networks and Attention Networks for CW-Complexes Khorana, Rahul Machine Learning We present a novel framework for learning on CW-complex structured data points. Recent advances have discussed CW-complexes as ideal learning representations for problems in cheminformatics. However, there is a lack of available machine learning methods suitable for learning on CW-complexes. In this paper we develop notions of convolution and attention that are well defined for CW-complexes. These notions enable us to create the first Hodge informed neural network that can receive a CW-complex as input. We illustrate and interpret this framework in the context of supervised prediction. |
| title | CW-CNN & CW-AN: Convolutional Networks and Attention Networks for CW-Complexes |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.16686 |