most citedEvaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles

58 citations · 67 across the 7 of their papers we have counts for

collaborators

7 papers

cs.DC20211 cited

Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

Wenhong Tian, Minxian Xu, Guangyao Zhou +3

Load balancing is vital for the efficient and long-term operation of cloud data centers. With virtualization, post (reactive) migration of virtual machines after allocation is the…

cs.DC2021

BESS Aided Reconfigurable Energy Supply using Deep Reinforcement Learning for 5G and Beyond

Hao Yuan, Guoming Tang, Deke Guo +4

The year of 2020 has witnessed the unprecedented development of 5G networks, along with the widespread deployment of 5G base stations (BSs). Nevertheless, the enormous energy consu…

cs.LG2021

On the Difficulty of Generalizing Reinforcement Learning Framework for Combinatorial Optimization

Mostafa Pashazadeh, Kui Wu

Combinatorial optimization problems (COPs) on the graph with real-life applications are canonical challenges in Computer Science. The difficulty of finding quality labels for probl…

cs.CV202158 cited

Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles

Jindi Zhang, Yang Lou, Jianping Wang +3

In recent years, many deep learning models have been adopted in autonomous driving. At the same time, these models introduce new vulnerabilities that may compromise the safety of a…

cs.LG20216 cited

FedNILM: Applying Federated Learning to NILM Applications at the Edge

Yu Zhang, Guoming Tang, Qianyi Huang +3

Non-intrusive load monitoring (NILM) helps disaggregate the household's main electricity consumption to energy usages of individual appliances, thus greatly cutting down the cost i…

cs.CL20212 cited

Addressing the Vulnerability of NMT in Input Perturbations

Weiwen Xu, Ai Ti Aw, Yang Ding +2

Neural Machine Translation (NMT) has achieved significant breakthrough in performance but is known to suffer vulnerability to input perturbations. As real input noise is difficult…