CW-CNN & CW-AN: Convolutional Networks and Attention Networks for CW-Complexes
<|reference_start|>CW-CNN & CW-AN: Convolutional Networks and Attention Networks for CW-Complexes: 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.<|reference_end|>
@article{khorana2024cw-cnn,
    title={CW-CNN & CW-AN: Convolutional Networks and Attention Networks for
  CW-Complexes},
    author={Rahul Khorana},
    journal={arXiv preprint arXiv:2408.16686},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16686},
    primaryClass={cs.LG}
}