Summary of Block-operations: Using Modular Routing to Improve Compositional Generalization, by Florian Dietz et al.
Block-Operations: Using Modular Routing to Improve Compositional Generalization
by Florian Dietz, Dietrich Klakow
First submitted to arxiv on: 1 Aug 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 paper investigates the poor compositional generalization in neural networks, hypothesizing that it is caused by difficulties with learning effective routing. To address this issue, the authors propose a novel architectural component called Multiplexer, which is based on splitting activation tensors into uniformly sized blocks and encouraging modular routing through an inductive bias. Experimental results show that Multiplexers exhibit strong compositional generalization, outperforming Feed Forward Neural Networks (FNNs) and Transformers on both synthetic and realistic tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to figure out why neural networks sometimes don’t work well together. They think it’s because the networks have trouble figuring out how to connect different parts of themselves. To solve this problem, they came up with a new idea called Multiplexer. It helps the network break down big problems into smaller, more manageable pieces. The authors tested their Multiplexer on some simple and real-world tasks and found that it did much better than other types of networks. |
Keywords
» Artificial intelligence » Generalization
