2 citations · 3 across the 4 of their papers we have counts for
5 papers
A Tale of Two Models: Constructing Evasive Attacks on Edge Models
Wei Hao, Aahil Awatramani, Jiayang Hu +5
Full-precision deep learning models are typically too large or costly to deploy on edge devices. To accommodate to the limited hardware resources, models are adapted to the edge us…
Sampling Training Data for Continual Learning Between Robots and the Cloud
Sandeep Chinchali, Evgenya Pergament, Manabu Nakanoya +5
Today's robotic fleets are increasingly measuring high-volume video and LIDAR sensory streams, which can be mined for valuable training data, such as rare scenes of road constructi…
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…
Network Offloading Policies for Cloud Robotics: a Learning-based Approach
Sandeep Chinchali, Apoorva Sharma, James Harrison +6
Today's robotic systems are increasingly turning to computationally expensive models such as deep neural networks (DNNs) for tasks like localization, perception, planning, and obje…
Trevor: Automatic configuration and scaling of stream processing pipelines
Manu Bansal, Eyal Cidon, Arjun Balasingam +3
Operating a distributed data stream processing workload efficiently at scale is hard. The operator of the workload must parallelize and lay out tasks of the workload with resources…