CW-CNN & CW-AN: Convolutional Networks and Attention Networks for CW-Complexes
Abstract: 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 neural network that can receive a CW-complex as input. We illustrate and interpret this framework in the context of supervised prediction.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: - We update the literature review in line with the suggestions from Reviewer HeyR.
- These changes are typeset in blue text and are contained to subsection 2.4. We include all updated citations and discussion of the methodologies of all articles mentioned by Reviewer HeyR.
Assigned Action Editor: ~Frederic_Sala1
Submission Number: 2964
Loading