5 papers · 1 filter
Gradient Surgery for Physics-Informed Neural Networks
Thomas Borsani, Giuseppe Di Fatta
Physics-Informed Neural Networks (PINNs) are trained by optimising a composite objective that combines data fitting with physics-based constraints, typically resulting in a highly…
Operator-Theoretic Generalization Bounds for Multitask Deep Learning
Mahdi Mohammadigohari, Thomas Borsani, Giuseppe Di Fatta
We develop operator-theoretic generalization bounds for deep multi-output function classes by representing network layers as Koopman composition operators on vector-valued reproduc…
Brownian Kernel Ladders
Mahdi Mohammadigohari, Giuseppe Di Fatta, Giuseppe Nicosia +1
We introduce Brownian kernel ladders (BKLs), a recursive hierarchy of integral reproducing kernel Hilbert spaces built from linear functionals by repeatedly integrating Brownian pu…
On the Koopman-Based Generalization Bounds for Multi-Task Deep Learning
Mahdi Mohammadigohari, Giuseppe Di Fatta, Giuseppe Nicosia +1
The paper establishes generalization bounds for multitask deep neural networks using operator-theoretic techniques. The authors propose a tighter bound than those derived from conv…
Operator-Based Generalization Bound for Deep Learning: Insights on Multi-Task Learning
Mahdi Mohammadigohari, Giuseppe Di Fatta, Giuseppe Nicosia +1
This paper presents novel generalization bounds for vector-valued neural networks and deep kernel methods, focusing on multi-task learning through an operator-theoretic framework.…