1 citations · 1 across the 3 of their papers we have counts for
3 papers
Parameter-Efficient Interventions for Enhanced Model Merging
Marcin Osial, Daniel Marczak, Bartosz Zieliński
Model merging combines knowledge from task-specific models into a unified multi-task model to avoid joint training on all task data. However, current methods face challenges due to…
Exploring the Stability Gap in Continual Learning: The Role of the Classification Head
Wojciech Łapacz, Daniel Marczak, Filip Szatkowski +1
Continual learning (CL) has emerged as a critical area in machine learning, enabling neural networks to learn from evolving data distributions while mitigating catastrophic forgett…
MagMax: Leveraging Model Merging for Seamless Continual Learning
Daniel Marczak, Bartłomiej Twardowski, Tomasz Trzciński +1
This paper introduces a continual learning approach named MagMax, which utilizes model merging to enable large pre-trained models to continuously learn from new data without forget…