4 papers
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.…
Gradient Similarity Surgery in Multi-Task Deep Learning
Thomas Borsani, Andrea Rosani, Giuseppe Nicosia +1
The multi-task learning () paradigm aims to simultaneously learn multiple tasks within a single model capturing higher-level, more general hidden patterns that are shared by t…