- Conference Paper
We propose an approach to learning with graph-structured data in the problem domain of graph classification. In particular, we present a novel type of readout operation to aggregate node features into a graph-level representation. To this end, we leverage persistent homology computed via a real-valued, learnable, filter function. We establish the theoretical foundation for differentiating through the persistent homology computation. Empirically, we show that this type of readout operation compares favorably to previous techniques, especially when the graph connectivity structure is informative for the learning problem. Show more
Book titleProceedings of the 37th International Conference on Machine Learning
Journal / seriesProceedings of Machine Learning Research
Pages / Article No.
NotesDue to the Coronavirus (COVID-19) the conference was conducted virtually.
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