2 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
ML-OPF-Bench: Benchmarking Machine Learning for Optimal Power Flow
Xinyi Liu, Xuan He, Danny H. K. Tsang +1
Machine Learning (ML) methods promise a fast solution process for Optimal Power Flow (OPF). While inconsistent test cases, implementations, and evaluation metrics across existing s…
DARTS: Distribution-Aware Active Rollout Trajectory Shaping for Accelerating LLM Reinforcement Learning
Yujie Wang, Siwei Chen, Longzan Luo +4
Reinforcement Learning (RL) has become pivotal for improving model capabilities yet suffers from rollout efficiency bottlenecks due to the long-tail response length distribution. W…
LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training
Xinyi Liu, Yujie Wang, Fangcheng Fu +4
Expert parallelism is vital for effectively training Mixture-of-Experts (MoE) models, enabling different devices to host distinct experts, with each device processing different inp…
Scaling Laws of Machine Learning for Optimal Power Flow
Xinyi Liu, Xuan He, Yize Chen
Optimal power flow (OPF) is one of the fundamental tasks for power system operations. While machine learning (ML) approaches such as deep neural networks (DNNs) have been widely st…