Summary of Learning Latent Graph Structures and Their Uncertainty, by Alessandro Manenti et al.
Learning Latent Graph Structures and their Uncertainty
by Alessandro Manenti, Daniele Zambon, Cesare Alippi
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to Graph Neural Networks (GNNs) is presented, focusing on the importance of relational information in enhancing model accuracy. The study highlights that minimizing a point-prediction loss function may not guarantee proper learning of latent relational information and its associated uncertainty. Instead, a suitable loss function on stochastic model outputs can achieve both goals simultaneously. A sampling-based method is proposed to solve this joint learning task, which is empirically validated to be effective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs use relationships to make predictions more accurate. Researchers have tried to learn these relationships while making predictions. This paper shows that just minimizing a loss function isn’t enough to learn the relationships correctly. Instead, we need to design a loss function that takes into account both the relationships and the uncertainty of those relationships. The paper proposes a new method to do this, which is tested and shown to be effective. |
Keywords
» Artificial intelligence » Loss function
