Summary of Recurrent Neural Networks: Vanishing and Exploding Gradients Are Not the End Of the Story, by Nicolas Zucchet et al.
Recurrent neural networks: vanishing and exploding gradients are not the end of the story
by Nicolas Zucchet, Antonio Orvieto
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
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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 Recurrent neural networks (RNNs) struggle with long-term memories due to vanishing and exploding gradients. The success of state-space models (SSMs), a subclass of RNNs, challenges our understanding. This paper explores optimization challenges in RNNs, finding that increasing memory leads to large output variations, making gradient-based learning sensitive. Our analysis reveals the importance of element-wise recurrence design patterns and parametrizations in mitigating this effect, present in SSMs and other architectures like LSTMs. These insights explain difficulties in gradient-based learning of RNNs and why some perform better than others. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recurrent neural networks have trouble remembering things for a long time. This is because the math behind how they learn gets really complicated as they try to remember longer and longer. Some special kinds of these networks, called state-space models, are actually good at this. We looked into why this might be happening and found that when these networks get better at remembering, their outputs start changing a lot. This makes it hard for them to learn new things. We think that the way these networks are built helps with this problem. |
Keywords
» Artificial intelligence » Optimization
