15 citations · 15 across the 3 of their papers we have counts for
4 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…
From Diet to Free Lunch: Estimating Auxiliary Signal Properties using Dynamic Pruning Masks in Speech Enhancement Networks
Riccardo Miccini, Clément Laroche, Tobias Piechowiak +2
Speech Enhancement (SE) in audio devices is often supported by auxiliary modules for Voice Activity Detection (VAD), SNR estimation, or Acoustic Scene Classification to ensure robu…
Data Aware Differentiable Neural Architecture Search for Tiny Keyword Spotting Applications
Yujia Shi, Emil Njor, Pablo Martínez-Nuevo +2
The success of Machine Learning is increasingly tempered by its significant resource footprint, driving interest in efficient paradigms like TinyML. However, the inherent complexit…
LEACH-RLC: Enhancing IoT Data Transmission with Optimized Clustering and Reinforcement Learning
F. Fernando Jurado-Lasso, J. F. Jurado, Xenofon Fafoutis
Wireless Sensor Networks (WSNs) play a pivotal role in enabling Internet of Things (IoT) devices with sensing and actuation capabilities. Operating in remote and resource-constrain…