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cs.LG2025
Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning
Yongkang Liu, Xingle Xu, Ercong Nie +5
Parameter-Efficient Fine-Tuning (PEFT) methods achieve performance comparable to Full Fine-Tuning (FFT) while requiring significantly fewer computing resources, making it the go-to…
cs.LG2024
Learn it or Leave it: Module Composition and Pruning for Continual Learning
Mingyang Wang, Heike Adel, Lukas Lange +2
In real-world environments, continual learning is essential for machine learning models, as they need to acquire new knowledge incrementally without forgetting what they have alrea…