4 citations · 7 across the 3 of their papers we have counts for
4 papers
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…
Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation
Emil Njor, Colby Banbury, Xenofon Fafoutis
Tiny machine learning (TinyML) promises to revolutionize fields such as healthcare, environmental monitoring, and industrial maintenance by running machine learning models on low-p…
Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications
Colby Banbury, Emil Njor, Andrea Mattia Garavagno +7
Tiny machine learning (TinyML) co-locates models with sensors on microcontrollers, where small models (which are disproportionately sensitive to label noise) and bespoke binary tas…
Data Aware Neural Architecture Search
Emil Njor, Jan Madsen, Xenofon Fafoutis
Neural Architecture Search (NAS) is a popular tool for automatically generating Neural Network (NN) architectures. In early NAS works, these tools typically optimized NN architectu…