Paper accepted at ICLR 2024

Our paper “Learning From Simplicial Data Based on Random Walks and 1D Convolutions” has been accepted at ICLR 2024.

In this paper, we propose a learning algorithm on topological domains based on random walks, which are processed by 1D convolutional neural networks. We show that this approach outperforms existing methods such as SCNN and MPSN on several datasets.

The paper is available on OpenReview and source code on GitLab.