Summary of Learning to Learn Without Forgetting Using Attention, by Anna Vettoruzzo et al.
Learning to Learn without Forgetting using Attention
by Anna Vettoruzzo, Joaquin Vanschoren, Mohamed-Rafik Bouguelia, Thorsteinn Rögnvaldsson
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This research paper proposes a new approach to machine learning called Continual Learning (CL), which enables models to learn from new information while retaining previously learned knowledge. The current methods in machine learning are prone to forgetting previously learned patterns, so the proposed meta-learning algorithm updates model parameters selectively and carefully to avoid unnecessary forgetting. The transformer-based optimizer uses attention mechanisms to learn complex relationships between model parameters across multiple tasks, generating effective weight updates for the current task while preventing catastrophic forgetting on previous tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way for machines to learn called Continual Learning. Right now, machine learning models forget what they learned before when they’re trained on something new. The researchers want to change this by developing an algorithm that helps models remember what they’ve learned in the past. They created a special kind of optimizer that uses attention to figure out how different parts of the model are connected and how they should be updated. This means that the model can learn from new information without forgetting what it knew before. |
Keywords
» Artificial intelligence » Attention » Continual learning » Machine learning » Meta learning » Transformer
