2 citations · 4 across the 6 of their papers we have counts for
4 papers · 1 filter
No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspaces
Daniel Marczak, Simone Magistri, Sebastian Cygert +3
Model merging integrates the weights of multiple task-specific models into a single multi-task model. Despite recent interest in the problem, a significant performance gap between…
Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning
Dipam Goswami, Simone Magistri, Kai Wang +3
Using pre-trained models has been found to reduce the effect of data heterogeneity and speed up federated learning algorithms. Recent works have explored training-free methods usin…
How green is continual learning, really? Analyzing the energy consumption in continual training of vision foundation models
Tomaso Trinci, Simone Magistri, Roberto Verdecchia +1
With the ever-growing adoption of AI, its impact on the environment is no longer negligible. Despite the potential that continual learning could have towards Green AI, its environm…
An Empirical Analysis of Forgetting in Pre-trained Models with Incremental Low-Rank Updates
Albin Soutif--Cormerais, Simone Magistri, Joost van de Weijer +1
Broad, open source availability of large pretrained foundation models on the internet through platforms such as HuggingFace has taken the world of practical deep learning by storm.…