13 citations · 24 across the 7 of their papers we have counts for
7 papers
Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning
Shanglin Zhou, Mikhail A. Bragin, Lynn Pepin +3
Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline significantly increases the…
Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles
Songyang Han, Shanglin Zhou, Lynn Pepin +3
The recent advancements in wireless technology enable connected autonomous vehicles (CAVs) to gather data via vehicle-to-vehicle (V2V) communication, such as processed LIDAR and ca…
Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems under Demand and Supply Uncertainties
Sihong He, Zhili Zhang, Shuo Han +5
Electric vehicles (EVs) are being rapidly adopted due to their economic and societal benefits. Autonomous mobility-on-demand (AMoD) systems also embrace this trend. However, the lo…
Data-Driven Distributionally Robust Electric Vehicle Balancing for Mobility-on-Demand Systems under Demand and Supply Uncertainties
Sihong He, Lynn Pepin, Guang Wang +2
As electric vehicle (EV) technologies become mature, EV has been rapidly adopted in modern transportation systems, and is expected to provide future autonomous mobility-on-demand (…
Botnets Breaking Transformers: Localization of Power Botnet Attacks Against the Distribution Grid
Lynn Pepin, Lizhi Wang, Jiangwei Wang +5
Traditional botnet attacks leverage large and distributed numbers of compromised internet-connected devices to target and overwhelm other devices with internet packets. With increa…
Enabling Retrain-free Deep Neural Network Pruning using Surrogate Lagrangian Relaxation
Deniz Gurevin, Shanglin Zhou, Lynn Pepin +4
Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline, i.e., training, pruning an…
A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles
Songyang Han, Shanglin Zhou, Jiangwei Wang +4
The recent advancements in wireless technology enable connected autonomous vehicles (CAVs) to gather information about their environment by vehicle-to-vehicle (V2V) communication.…