Summary of Data-dependent and Oracle Bounds on Forgetting in Continual Learning, by Lior Friedman et al.
Data-dependent and Oracle Bounds on Forgetting in Continual Learning
by Lior Friedman, Ron Meir
First submitted to arxiv on: 13 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 The paper presents a theoretical framework for quantifying and bounding the degree of forgetting in continual learning settings, where knowledge must be preserved and reused between tasks. The authors derive both data-dependent and oracle upper bounds that apply regardless of model or algorithm choice, as well as bounds for Gibbs posteriors. They also propose an algorithm based on their bounds and demonstrate its effectiveness in several continual learning problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Continual learning is like trying to remember what you learned in school last year. The goal is to keep the good stuff and not forget it when you move on to new topics. Scientists have developed some practical ways to do this, but they haven’t really thought about how well these methods work or why we might forget things. This paper helps fill that gap by providing some rules for how much forgetting can happen in different situations. The authors also share an algorithm that works well and shows it’s effective in real-world problems. |
Keywords
* Artificial intelligence * Continual learning
