Summary of Xb-maml: Learning Expandable Basis Parameters For Effective Meta-learning with Wide Task Coverage, by Jae-jun Lee et al.
XB-MAML: Learning Expandable Basis Parameters for Effective Meta-Learning with Wide Task Coverage
by Jae-Jun Lee, Sung Whan Yoon
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces XB-MAML, a novel approach to meta-learning that learns expandable basis parameters. This allows it to form an effective initialization model that can adapt to widely-ranging tasks and domains. The method observes the discrepancy between the vector space spanned by the basis and fine-tuned parameters to decide whether to expand the basis. XB-MAML outperforms existing works in multi-domain meta-learning benchmarks, opening up new possibilities for obtaining diverse inductive bias. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Meta-learning is a way for machines to learn how to learn from experience. This helps them solve new problems they haven’t seen before. One problem with this approach is that it can be tricky to adapt to very different situations. The authors of this paper have created a new method called XB-MAML that tries to solve this problem by learning special “basis” parameters that can be combined in different ways to make an effective starting point for solving new tasks. |
Keywords
* Artificial intelligence * Meta learning * Vector space
