2 citations · 4 across the 6 of their papers we have counts for
8 papers
Accurate and Efficient Low-Rank Model Merging in Core Space
Aniello Panariello, Daniel Marczak, Simone Magistri +5
In this paper, we address the challenges associated with merging low-rank adaptations of large neural networks. With the rise of parameter-efficient adaptation techniques, such as…
EFC++: Elastic Feature Consolidation with Prototype Re-balancing for Cold Start Exemplar-free Incremental Learning
Simone Magistri, Tomaso Trinci, Albin Soutif-Cormerais +2
Exemplar-free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper, we consider the challenging Cold…
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.…
Elastic Feature Consolidation for Cold Start Exemplar-Free Incremental Learning
Simone Magistri, Tomaso Trinci, Albin Soutif-Cormerais +2
Exemplar-Free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper, we consider the challenging Cold…
A Bi-Objective Optimization Based Acquisition Strategy for Batch Bayesian Global Optimization
Francesco Carciaghi, Simone Magistri, Pierluigi Mansueto +1
In this paper, we deal with batch Bayesian Optimization (Bayes-Opt) problems over a box and we propose a novel bi-objective optimization (BOO) acquisition strategy to sample points…