6 citations · 10 across the 12 of their papers we have counts for
4 papers · 1 filter
What changes after deployment? A survey on On-device Learning in TinyML
Massimo Pavan, Luca Pezzarossa, Fabrizio Pittorino +2
Machine learning models on microcontroller-class devices (TinyML) face a fundamental challenge: post-deployment distribution change undermines static models. On-device learning (OD…
HERCULES: Hardware-Efficient, Robust, Continual Learning Neural Architecture Search
Matteo Gambella, Fabrizio Pittorino, Manuel Roveri
Neural Architecture Search (NAS) has emerged as a powerful framework for automatically discovering neural architectures that balance accuracy and efficiency. However, as AI transit…
Active In-Context Learning for Tabular Foundation Models
Wilailuck Treerath, Fabrizio Pittorino
Active learning (AL) reduces labeling cost by querying informative samples, but in tabular settings its cold-start gains are often limited because uncertainty estimates are unrelia…
SQUAD: Scalable Quorum Adaptive Decisions via ensemble of early exit neural networks
Matteo Gambella, Fabrizio Pittorino, Giuliano Casale +1
Early-exit neural networks have become popular for reducing inference latency by allowing intermediate predictions when sufficient confidence is achieved. However, standard approac…