Summary of Hyperparameter Selection in Continual Learning, by Thomas L. Lee et al.
Hyperparameter Selection in Continual Learning
by Thomas L. Lee, Sigrid Passano Hellan, Linus Ericsson, Elliot J. Crowley, Amos Storkey
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper investigates the optimal approach to hyperparameter optimization (HPO) in continual learning (CL), where learners train on a stream of data. Standard HPO methods are not applicable in CL as they require access to all data at once. The authors examine several realistic HPO frameworks, comparing their performance on popular CL benchmarks. Surprisingly, no single framework outperforms the others consistently, suggesting that practitioners should consider factors beyond performance when selecting an HPO method. This includes compute efficiency and other practical considerations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to find the best way to adjust settings in machine learning models as they learn from new data over time. Since these models can’t see all the data at once, special methods are needed to optimize their performance. The authors compare different approaches for optimizing model settings and find that none of them work better than others on common benchmarks. Instead, practitioners should consider factors like how fast the method is and choose one based on what’s important in their specific situation. |
Keywords
» Artificial intelligence » Continual learning » Hyperparameter » Machine learning » Optimization
