Summary of Any2graph: Deep End-to-end Supervised Graph Prediction with An Optimal Transport Loss, by Paul Krzakala et al.
Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
by Paul Krzakala, Junjie Yang, Rémi Flamary, Florence d’Alché-Buc, Charlotte Laclau, Matthieu Labeau
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 propose Any2graph, a versatile framework for supervised graph prediction that can handle any type of input. The model is built on a novel loss function called Partially-Masked Fused Gromov-Wasserstein, which exhibits properties such as permutation invariance, differentiability, and scalability. The authors demonstrate the effectiveness of Any2graph by comparing it to existing competitors on a range of tasks, including map construction from satellite images and molecule prediction from fingerprint data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Any2graph is a new way for computers to predict entire graphs based on any kind of input. This helps with things like making maps from satellite pictures or predicting what molecules are in a mixture. The researchers created a special way to measure how well the predictions do, called Partially-Masked Fused Gromov-Wasserstein. They tested Any2graph and it did better than other methods on some big tasks. |
Keywords
* Artificial intelligence * Loss function * Supervised
