8 papers
Assessing Sample Quality in Conditional Generation under Compositional Shift
Berker Demirel, Valentino Maiorca, Marco Fumero +2
Conditional generators provide a natural tool for controllable generation, including settings where the desired condition is a new composition of observed attributes or experimenta…
HyperTransport: Amortized Conditioning of T2I Generative Models
Valentino Maiorca, Eleonora Gualdoni, Xavier Suau +3
As foundation models grow in capability, the ability to efficiently and reliably control their behavior becomes critical. Fine-tuning these models can be costly, and while promptin…
Head Pursuit: Probing Attention Specialization in Multimodal Transformers
Lorenzo Basile, Valentino Maiorca, Diego Doimo +2
Language and vision-language models have shown impressive performance across a wide range of tasks, but their internal mechanisms remain only partly understood. In this work, we st…
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…
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…
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…