2 papers
math.NA2026
Model reduction of parametric ordinary differential equations via autoencoders: representation properties and convergence analysis
Enrico Ballini, Marco Gambarini, Alessio Fumagalli +3
We propose a reduced-order modeling approach for nonlinear, parameter-dependent ordinary differential equations (ODE). Dimensionality reduction is achieved using nonlinear maps rep…
cs.LG2026
Elimination-compensation pruning for fully-connected neural networks
Enrico Ballini, Luca Muscarnera, Alessio Fumagalli +2
The unmatched ability of Deep Neural Networks in capturing complex patterns in large and noisy datasets is often associated with their large hypothesis space, and consequently to t…