Summary of Recurrent Aggregators in Neural Algorithmic Reasoning, by Kaijia Xu et al.
Recurrent Aggregators in Neural Algorithmic Reasoning
by Kaijia Xu, Petar Veličković
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper challenges the traditional design choice in graph neural networks (GNNs) used in neural algorithmic reasoning (NAR) by replacing the equivariant aggregation function with a recurrent neural network. This approach is shown to be effective when nodes have a natural ordering, which is often the case in established reasoning benchmarks like CLRS-30. The recurrent NAR (RNAR) model achieves state-of-the-art results on tasks like Heapsort and Quickselect, demonstrating its potential for handling complex tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper takes a fresh look at neural algorithmic reasoning by using recurrent networks instead of traditional graph neural networks. This change makes sense when the nodes have a natural order, which is often the case in certain problems. The new approach does very well on these types of problems and even beats current records on some tasks. |
Keywords
» Artificial intelligence » Neural network
