Research Paper#Transfer Learning, Multi-task Learning, Machine Learning🔬 ResearchAnalyzed: Jan 3, 2026 08:37
Characterizing Transfer Learning with Multi-task Learning Curves
Analysis
This paper proposes a novel method to characterize transfer learning effects by analyzing multi-task learning curves. Instead of focusing on model updates, the authors perturb the dataset size to understand how performance changes. This approach offers a potentially more fundamental understanding of transfer, especially in the context of foundation models. The use of learning curves allows for a quantitative assessment of transfer effects, including pairwise and contextual transfer.
Key Takeaways
- •Proposes a method to characterize transfer learning using multi-task learning curves.
- •Focuses on perturbing the dataset size rather than model updates.
- •Offers a quantitative approach to assess transfer effects.
- •Evaluated on a drug-target interaction dataset.
- •Highlights the ability to delineate pairwise and contextual transfer effects.
Reference
“Learning curves can better capture the effects of multi-task learning and their multi-task extensions can delineate pairwise and contextual transfer effects in foundation models.”