Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 10·5 min read
One Loop, Two Gains: Can Active Learning win the Lottery for Free?
TL;DR
A new method called Improve & Prune (I&P) integrates magnitude pruning into active learning cycles, yielding sparse models that match the accuracy of dense networks. This approach significantly reduces computational overhead while achieving up to 95% sparsity.
✦ Why It Matters
Implement I&P in your active learning workflows to reduce computational costs and improve model efficiency.
Key Takeaways
- Integrate I&P to reduce retraining costs in active learning.
- Achieve up to 95% model sparsity without sacrificing accuracy.
- Utilize I&P for large-scale datasets and architectures.
- Explore various acquisition functions to optimize active learning.
How It Works
I&P leverages the iterative retraining structure of active learning to incorporate magnitude pruning seamlessly. By pruning weights during each retraining cycle, it identifies and retains only the most critical parameters, effectively creating sparse models that can be trained on new data without starting from scratch.