Summary of Hierarchically Gated Experts For Efficient Online Continual Learning, by Kevin Luong and Michael Thielscher
Hierarchically Gated Experts for Efficient Online Continual Learning
by Kevin Luong, Michael Thielscher
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes two novel approaches to online continual learning: Gated Experts (GE) and Hierarchically Gated Experts (HGE). The goal is to learn a set of tasks without access to previous data, while tasks arrive sequentially. GE uses a dynamically growing set of experts to acquire new knowledge without catastrophic forgetting. HGE extends this approach by organizing experts into a hierarchical structure for efficient expert selection. Both methods are evaluated on standard continual learning benchmarks and achieve comparable results with current state-of-the-art approaches, with HGE being more efficient. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to learn new skills one at a time, but each skill builds upon the previous ones. That’s what online continual learning is all about. The paper introduces two new ways to do this: Gated Experts and Hierarchically Gated Experts. These methods help computers learn and remember new things without forgetting what they already know. They’re tested on standard benchmarks and perform well, with one method being more efficient than the other. |
Keywords
» Artificial intelligence » Continual learning
