Summary of Amosl: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation, by Peiyu Liang and Hongchang Gao and Xubin He
AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation
by Peiyu Liang, Hongchang Gao, Xubin He
First submitted to arxiv on: 4 Jun 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 This research paper proposes a novel approach called Adaptive Modality-wise Structure Learning (AMoSL) to improve Multi-view Graph Neural Networks’ (MVGNNs) performance in leveraging diverse modalities for object representation. AMoSL addresses the limitation of existing MVGNNs by assuming identical local topology structures across modalities, which is often unrealistic in real-world scenarios. The authors employ optimal transport to capture node correspondences between modalities and jointly learn with graph embedding, enabling efficient end-to-end training. Additionally, AMoSL adapts to downstream tasks through unsupervised learning on inter-modality distances. Experimental results demonstrate the effectiveness of AMoSL in training more accurate graph classifiers on six benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way called Adaptive Modality-wise Structure Learning (AMoSL) to help computers understand objects better when they have different types of information about them. Right now, some computer programs can combine this information well, but only if the details are very similar. AMoSL makes it possible for these programs to work even when the details are quite different. It does this by finding connections between the different pieces of information and learning how to use all of it together. This is useful because it allows computers to make more accurate decisions about what they’re looking at. |
Keywords
» Artificial intelligence » Embedding » Unsupervised
