Summary of Graph Knowledge Distillation to Mixture Of Experts, by Pavel Rumiantsev and Mark Coates
Graph Knowledge Distillation to Mixture of Experts
by Pavel Rumiantsev, Mark Coates
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper explores the use of Graph Neural Networks (GNNs) for node classification, finding that they offer high accuracy. However, GNNs are limited by latency issues due to neighborhood processing operations. To address this, researchers have turned to knowledge distillation from a trained GNN to a Multi-Layer Perceptron (MLP). While promising, existing techniques struggle with inconsistent performance in transductive and inductive settings. The proposed Routing-by-Memory (RbM) model tackles these concerns by introducing a Mixture-of-Experts (MoE) architecture that encourages expert specialization. By doing so, RbM achieves consistent performance across multiple datasets. This work contributes to the development of more efficient and effective graph neural networks for node classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special kinds of artificial intelligence called Graph Neural Networks (GNNs) to classify nodes in graphs. GNNs are really good at this task, but there’s a problem – they take too long because they have to look at all the connections between nodes. To fix this, people try to “teach” a simpler model how to do what the GNN can do. The problem is that these simpler models don’t always work well. This paper proposes a new way of doing this teaching called Routing-by-Memory (RbM). RbM uses multiple smaller models that each specialize in certain tasks, which makes it really good at node classification. |
Keywords
» Artificial intelligence » Classification » Gnn » Knowledge distillation » Mixture of experts
