Summary of Attention As a Hypernetwork, by Simon Schug et al.
Attention as a Hypernetwork
by Simon Schug, Seijin Kobayashi, Yassir Akram, João Sacramento, Razvan Pascanu
First submitted to arxiv on: 9 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 Transformers can generalize to novel problem instances by using mechanisms that allow them to perform tasks learned during training on new, unseen compositions of these tasks. By reformulating the multi-head attention mechanism as a hypernetwork, researchers discovered a low-dimensional latent code that specifies key-query specific operations and is predictive of the subtasks the network performs on unseen task compositions. This means that the latent codes acquired during training are reused to solve unseen problem instances. To further examine this hypothesis, the researchers modified the hypernetwork-generated linear value network to make it nonlinear, which improved compositional generalization on abstract reasoning tasks, such as a symbolic version of the Raven’s Progressive Matrices human intelligence test. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers can learn new things by combining things they already know. This is called compositional generalization. It means that even if the transformer didn’t see a specific problem before, it can still solve it because it learned to break down complex problems into smaller parts and reuse this knowledge for new tasks. Researchers found that a special mechanism in transformers helps them do this by creating a low-dimensional code that predicts what they will do on new tasks. This code is reused when the transformer sees new problems it hasn’t seen before. |
Keywords
» Artificial intelligence » Generalization » Multi head attention » Transformer
