62 citations · 142 across the 18 of their papers we have counts for
6 papers · 1 filter
Machine Learning Sensors
Pete Warden, Matthew Stewart, Brian Plancher +6
Machine learning sensors represent a paradigm shift for the future of embedded machine learning applications. Current instantiations of embedded machine learning (ML) suffer from c…
Personalized Federated Learning of Driver Prediction Models for Autonomous Driving
Manabu Nakanoya, Junha Im, Hang Qiu +3
Autonomous vehicles (AVs) must interact with a diverse set of human drivers in heterogeneous geographic areas. Ideally, fleets of AVs should share trajectory data to continually re…
Data Sharing and Compression for Cooperative Networked Control
Jiangnan Cheng, Marco Pavone, Sachin Katti +2
Sharing forecasts of network timeseries data, such as cellular or electricity load patterns, can improve independent control applications ranging from traffic scheduling to power g…
Characterizing and Taming Model Instability Across Edge Devices
Eyal Cidon, Evgenya Pergament, Zain Asgar +2
The same machine learning model running on different edge devices may produce highly-divergent outputs on a nearly-identical input. Possible reasons for the divergence include diff…
Multi-agent Reinforcement Learning for Networked System Control
Tianshu Chu, Sandeep Chinchali, Sachin Katti
This paper considers multi-agent reinforcement learning (MARL) in networked system control. Specifically, each agent learns a decentralized control policy based on local observatio…
Bandana: Using Non-volatile Memory for Storing Deep Learning Models
Assaf Eisenman, Maxim Naumov, Darryl Gardner +5
Typical large-scale recommender systems use deep learning models that are stored on a large amount of DRAM. These models often rely on embeddings, which consume most of the require…