no code implementations • 9 Nov 2022 • Yuanlong Li, Gaopan Huang, Min Zhou, Chuan Fu, Honglin Qiao, Yan He
Learning an explainable classifier often results in low accuracy model or ends up with a huge rule set, while learning a deep model is usually more capable of handling noisy data at scale, but with the cost of hard to explain the result and weak at generalization.
no code implementations • 26 Oct 2021 • Lulu Pan, Haibin Shao, Yuanlong Li, Dewei Li, Yugeng Xi
The Zeno phenomenon can be excluded for both cases under the proposed coordination strategy.
1 code implementation • 24 May 2018 • Yuanlong Li, Linsen Dong, Xin Zhou, Yonggang Wen, Kyle Guan
Model-based reinforcement learning (MBRL) has been proposed as a promising alternative solution to tackle the high sampling cost challenge in the canonical reinforcement learning (RL), by leveraging a learned model to generate synthesized data for policy training purpose.
no code implementations • 15 Sep 2017 • Yuanlong Li, Yonggang Wen, Kyle Guan, DaCheng Tao
Specifically, we propose an end-to-end cooling control algorithm (CCA) that is based on the actor-critic framework and an off-policy offline version of the deep deterministic policy gradient (DDPG) algorithm.
no code implementations • 15 Aug 2016 • Yuanlong Li, Han Hu, Yonggang Wen, Jun Zhang
Finally, using the power consumption data from a real data center, we show that the proposed LTW can improve the classification accuracy of DTW from about 84% to 90%.