Summary of Convergence Of Manifold Filter-combine Networks, by David R. Johnson et al.
Convergence of Manifold Filter-Combine Networks
by David R. Johnson, Joyce Chew, Siddharth Viswanath, Edward De Brouwer, Deanna Needell, Smita Krishnaswamy, Michael Perlmutter
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce Manifold Filter-Combine Networks (MFCNs) to better understand manifold neural networks (MNNs). MFCNs use a filter-combine framework similar to aggregate-combine paradigms for graph neural networks (GNNs), leading to new families of MNNs. The authors propose a method for implementing MFCNs on high-dimensional point clouds by approximating the manifold with a sparse graph, showing that this approach is consistent and converges to a continuum limit as the number of data points increases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Manifold neural networks (MNNs) are a type of artificial intelligence designed to work with complex data. Researchers created a new kind of MNN called Manifold Filter-Combine Networks (MFCNs). These networks have a special way of combining information, similar to how graph neural networks combine information for images. The authors showed that their method works well on big datasets and gets closer to the ideal result as more data is added. |
