Summary of Memory-reduced Meta-learning with Guaranteed Convergence, by Honglin Yang et al.
Memory-Reduced Meta-Learning with Guaranteed Convergence
by Honglin Yang, Ji Ma, Xiao Yu
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 optimization-based meta-learning approach is gaining popularity due to its ability to quickly adapt to new tasks with limited data. However, current approaches like MAML and ANIL rely on backpropagation for upper-level gradient estimation, which requires storing historical parameters and gradients, increasing computational and memory overhead. This paper proposes a novel meta-learning algorithm that avoids using historical parameters and significantly reduces memory costs per iteration. Additionally, it proves sublinear convergence with iteration number and decaying convergence error with batch size. In deterministic meta-learning, the algorithm converges to an exact solution. Experimental results on benchmarks confirm its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way for machines to learn quickly from small amounts of data. Existing methods use a lot of memory and computation to make decisions. The researchers propose a new method that uses much less memory and computation, but still works well. They show that their method gets better with more iterations and smaller batches of data. This is important because it means computers can learn faster and more efficiently. |
Keywords
» Artificial intelligence » Backpropagation » Meta learning » Optimization
