no code implementations • 14 May 2024 • Jingwen Wang, Dehui Du, Yida Li, Yiyang Li, Yikang Chen
Several experiments demonstrate the feasibility of our approach in common environments, confirming its ability to enhance the effectiveness of DRL training and impart a certain level of explainability to the training process.
no code implementations • 13 Sep 2023 • YuanHao Liu, Dehui Du, Zihan Jiang, Anyan Huang, Yiyang Li
To address these challenges, we propose a novel framework called Mining Causal Natural Structure (MCNS), which is automatic and domain-agnostic and helps to find the causal natural structures inside time series via the internal causality scheme.
1 code implementation • 14 Sep 2022 • Yanyun Wang, Dehui Du, YuanHao Liu
And a case study shows it can not only find the ideal model reducing 0. 53% of dangerous cases by only sacrificing 0. 04% of training accuracy, but also refine the learning rate to train a new model averagely outperforming the original one with a 1. 62% lower value of itself and 0. 36% fewer number of dangerous cases.
2 code implementations • 14 Sep 2022 • Yanyun Wang, Dehui Du, Haibo Hu, Zi Liang, YuanHao Liu
Recent years have witnessed the success of recurrent neural network (RNN) models in time series classification (TSC).