28 citations · 49 across the 17 of their papers we have counts for
5 papers · 1 filter
ResiDual Transformer Alignment with Spectral Decomposition
Lorenzo Basile, Valentino Maiorca, Luca Bortolussi +2
When examined through the lens of their residual streams, a puzzling property emerges in transformer networks: residual contributions (e.g., attention heads) sometimes specialize i…
Latent Space Translation via Inverse Relative Projection
Valentino Maiorca, Luca Moschella, Marco Fumero +2
The emergence of similar representations between independently trained neural models has sparked significant interest in the representation learning community, leading to the devel…
Scalable unsupervised alignment of general metric and non-metric structures
Sanketh Vedula, Valentino Maiorca, Lorenzo Basile +2
Aligning data from different domains is a fundamental problem in machine learning with broad applications across very different areas, most notably aligning experimental readouts i…
Latent Functional Maps: a spectral framework for representation alignment
Marco Fumero, Marco Pegoraro, Valentino Maiorca +2
Neural models learn data representations that lie on low-dimensional manifolds, yet modeling the relation between these representational spaces is an ongoing challenge. By integrat…
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…