3 citations · 3 across the 1 of their papers we have counts for
5 papers
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…
TActiLE: Tiny Active LEarning for wearable devices
Massimo Pavan, Claudio Galimberti, Manuel Roveri
Tiny Machine Learning (TinyML) algorithms have seen extensive use in recent years, enabling wearable devices to be not only connected but also genuinely intelligent by running mach…
EmbBERT: Attention Under 2 MB Memory
Riccardo Bravin, Massimo Pavan, Hazem Hesham Yousef Shalby +2
Transformer architectures based on the attention mechanism have revolutionized natural language processing (NLP), driving major breakthroughs across virtually every NLP task. Howev…
StreamTinyNet: video streaming analysis with spatial-temporal TinyML
Hazem Hesham Yousef Shalby, Massimo Pavan, Manuel Roveri
Tiny Machine Learning (TinyML) is a branch of Machine Learning (ML) that constitutes a bridge between the ML world and the embedded system ecosystem (i.e., Internet of Things devic…
TinySV: Speaker Verification in TinyML with On-device Learning
Massimo Pavan, Gioele Mombelli, Francesco Sinacori +1
TinyML is a novel area of machine learning that gained huge momentum in the last few years thanks to the ability to execute machine learning algorithms on tiny devices (such as Int…