3 papers
cs.LG2026
Navigating the Latent Space Dynamics of Neural Models
Marco Fumero, Luca Moschella, Emanuele Rodolà +1
Neural networks transform high-dimensional data into compact, structured representations, often modeled as elements of a lower dimensional latent space. In this paper, we present a…
cs.LG2025
Mapping representations in Reinforcement Learning via Semantic Alignment for Zero-Shot Stitching
Antonio Pio Ricciardi, Valentino Maiorca, Luca Moschella +2
Deep Reinforcement Learning (RL) models often fail to generalize when even small changes occur in the environment's observations or task requirements. Addressing these shifts typic…
cs.LG2025
R3L: Relative Representations for Reinforcement Learning
Antonio Pio Ricciardi, Valentino Maiorca, Luca Moschella +2
Visual Reinforcement Learning is a popular and powerful framework that takes full advantage of the Deep Learning breakthrough. It is known that variations in input domains (e.g., d…