89 citations · 273 across the 18 of their papers we have counts for
3 papers · 1 filter
HESSO: Towards Automatic Efficient and User Friendly Any Neural Network Training and Pruning
Tianyi Chen, Xiaoyi Qu, David Aponte +7
Structured pruning is one of the most popular approaches to effectively compress the heavy deep neural networks (DNNs) into compact sub-networks while retaining performance. The ex…
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…
MobileNetV4 -- Universal Models for the Mobile Ecosystem
Danfeng Qin, Chas Leichner, Manolis Delakis +11
We present the latest generation of MobileNets, known as MobileNetV4 (MNv4), featuring universally efficient architecture designs for mobile devices. At its core, we introduce the…