3 papers
cs.LG2026
Distilling Linearized Behavior into Non-Linear Fine-Tuning for Effective Task Arithmetic
Thomas Sommariva, Francesca Morandi, Simone Calderara +1
Task vector composition has emerged as a promising paradigm for editing pre-trained models, enabling model merging through addition and unlearning through subtraction. Fine-tuning…
cs.AI2026
Dataless Weight Disentanglement in Task Arithmetic via Kronecker-Factored Approximate Curvature
Angelo Porrello, Pietro Buzzega, Felix Dangel +4
Task Arithmetic yields a modular, scalable way to adapt foundation models. Combining multiple task vectors, however, can lead to cross-task interference, causing representation dri…
cs.NE2025
How to Train Your Metamorphic Deep Neural Network
Thomas Sommariva, Simone Calderara, Angelo Porrello
Neural Metamorphosis (NeuMeta) is a recent paradigm for generating neural networks of varying width and depth. Based on Implicit Neural Representation (INR), NeuMeta learns a conti…