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MetaAT: Active Testing for Label-Efficient Evaluation of Dense Recognition Tasks
In this study, we investigate the task of active testing for label-efficient evaluation, which aims to estimate a model’s …
Sanbao Su, Xin Li, Thang Doan, Sima Behpour, Wenbin He, Liang Gou, Fei Miao, Liu Ren
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What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?
Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed with the assumption that agents’ policies are …
Songyang Han, Sanbao Su, Sihong He, Shuo Han, Haizhao Yang, Fei Miao
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Uncertainty Quantification of Collaborative Detection for Self-Driving
Sharing information between connected and autonomous vehicles (CAVs) fundamentally improves the performance of collaborative object …
Sanbao Su, Yiming Li, Sihong He, Songyang Han, Chen Feng, Caiwen Ding, Fei Miao
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Uncertainty Quantification of Collaborative Detection for Self-Driving
Stable and Efficient Shapley Value-Based Reward Reallocation for Multi-Agent Reinforcement Learning of Autonomous Vehicles
With the development of sensing and communication technologies in networked cyber-physical systems (CPSs), multi-agent reinforcement …
Songyang Han, He Wang, Sanbao Su, Yuanyuan Shi, Fei Miao
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Efficient Batch Statistical Error Estimation for Iterative Multi-level Approximate Logic Synthesis
Approximate computing is an emerging energy-efficient paradigm for error-resilient applications. Approximate logic synthesis (ALS) is …
Sanbao Su, Yi Wu, Weikang Qian
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