11 citations · 45 across the 14 of their papers we have counts for
4 papers · 1 filter
Active Client Selection in Federated Trajectory Prediction with Uncertainty-Awareness and Heterogeneous Complexity
Yiming Xie, Muzi Peng, Fei Miao +2
Training sequence models such as transformers is now standard for autonomous vehicle trajectory prediction, yet assembling high-quality centralized datasets remains challenging bec…
Robust Multi-Agent Reinforcement Learning with State Uncertainty
Sihong He, Songyang Han, Sanbao Su +3
In real-world multi-agent reinforcement learning (MARL) applications, agents may not have perfect state information (e.g., due to inaccurate measurement or malicious attacks), whic…
Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles
Muzi Peng, Jiangwei Wang, Dongjin Song +2
Deep learning is the method of choice for trajectory prediction for autonomous vehicles. Unfortunately, its data-hungry nature implicitly requires the availability of sufficiently…
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…