no code implementations • 4 Nov 2023 • Bowen Song, Marco Paolieri, Harper E. Stewart, Leana Golubchik, Jill L. McNitt-Gray, Vishal Misra, Devavrat Shah
Our aim in this paper is to determine if data collected with inertial measurement units (IMUs), that can be worn by athletes during outdoor runs, can be used to predict GRF with sufficient accuracy to allow the analysis of its derived biomechanical variables (e. g., contact time and loading rate).
no code implementations • 6 Oct 2022 • Zhuojin Li, Marco Paolieri, Leana Golubchik
With the growing workload of inference tasks on mobile devices, state-of-the-art neural architectures (NAs) are typically designed through Neural Architecture Search (NAS) to identify NAs with good tradeoffs between accuracy and efficiency (e. g., latency).
no code implementations • ACM Transactions on Intelligent Systems and Technology 2022 2022 • Chien-Lun Chen, Sara Babakniya, Marco Paolieri, Leana Golubchik
Federated learning allows multiple users to collaboratively train a shared classification model while preserving data privacy.
no code implementations • 12 Jun 2020 • Chien-Lun Chen, Leana Golubchik, Marco Paolieri
Federated learning allows multiple users to collaboratively train a shared classification model while preserving data privacy.
no code implementations • 12 Nov 2019 • Zhuojin Li, Wumo Yan, Marco Paolieri, Leana Golubchik
Our approach is able to model the interaction of multiple nodes and the scheduling of concurrent transmissions between the parameter server and each node.