6 papers
SAEs Can Improve Unlearning: Dynamic Sparse Autoencoder Guardrails for Precision Unlearning in LLMs
Aashiq Muhamed, Jacopo Bonato, Mona Diab +1
Machine unlearning is a promising approach to improve LLM safety by removing unwanted knowledge from the model. However, prevailing gradient-based unlearning methods suffer from is…
Closed-form merging of parameter-efficient modules for Federated Continual Learning
Riccardo Salami, Pietro Buzzega, Matteo Mosconi +3
Model merging has emerged as a crucial technique in Deep Learning, enabling the integration of multiple models into a unified system while preserving perfor-mance and scalability.…
Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning
Matteo Mosconi, Andriy Sorokin, Aniello Panariello +6
The use of skeletal data allows deep learning models to perform action recognition efficiently and effectively. Herein, we believe that exploring this problem within the context of…
Is Retain Set All You Need in Machine Unlearning? Restoring Performance of Unlearned Models with Out-Of-Distribution Images
Jacopo Bonato, Marco Cotogni, Luigi Sabetta
In this paper, we introduce Selective-distillation for Class and Architecture-agnostic unleaRning (SCAR), a novel approximate unlearning method. SCAR efficiently eliminates specifi…
MIND: Multi-Task Incremental Network Distillation
Jacopo Bonato, Francesco Pelosin, Luigi Sabetta +1
The recent surge of pervasive devices that generate dynamic data streams has underscored the necessity for learning systems to adapt continually to data distributional shifts. To t…
DUCK: Distance-based Unlearning via Centroid Kinematics
Marco Cotogni, Jacopo Bonato, Luigi Sabetta +2
Machine Unlearning is rising as a new field, driven by the pressing necessity of ensuring privacy in modern artificial intelligence models. This technique primarily aims to eradica…