Summary of How More Data Can Hurt: Instability and Regularization in Next-generation Reservoir Computing, by Yuanzhao Zhang et al.
How more data can hurt: Instability and regularization in next-generation reservoir computing
by Yuanzhao Zhang, Edmilson Roque dos Santos, Sean P. Cornelius
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE); Dynamical Systems (math.DS); Adaptation and Self-Organizing Systems (nlin.AO)
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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 In this study, researchers investigate how excessive data can negatively impact the performance of deep neural networks and a specific type of reservoir computing models used to learn dynamics from data. They find that as more training data is added, these models can become unstable due to the introduction of auxiliary dimensions created by delayed states. To address this issue, they propose simple strategies for mitigating instability through increased regularization or introducing noise during training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recent research has shown that having too much data can actually harm deep neural networks’ performance. This study looks into a similar phenomenon in reservoir computing models used to learn dynamic systems from data. The researchers discovered that as more data is added, these models can become unstable due to the creation of auxiliary dimensions by delayed states. To fix this problem, they suggest two simple solutions: increasing regularization strength or adding noise during training. |
Keywords
» Artificial intelligence » Regularization
