no code implementations • 15 Jan 2024 • Tinghui Ouyang, AprilPyone MaungMaung, Koichi Konishi, Yoshiki Seo, Isao Echizen
In the era of large AI models, the complex architecture and vast parameters present substantial challenges for effective AI quality management (AIQM), e. g. large language model (LLM).
no code implementations • 12 Oct 2023 • Tinghui Ouyang, Isao Echizen, Yoshiki Seo
Aiming to investigate the data domain and out-of-distribution (OOD) data in AI quality management (AIQM) study, this paper proposes to use deep learning techniques for feature representation and develop a novel statistical measure for OOD detection.
no code implementations • 29 Jun 2022 • Zhenhao Tang, Shikui Wang, Xiangying Chai, Shengxian Cao, Tinghui Ouyang, Yang Li
An automatic encoder (AE) extreme learning machine (ELM)-AE-ELM model is proposed to predict the NOx emission concentration based on the combination of mutual information algorithm (MI), AE, and ELM.
1 code implementation • 7 Jan 2021 • Tinghui Ouyang, Vicent Sant Marco, Yoshinao Isobe, Hideki Asoh, Yutaka Oiwa, Yoshiki Seo
However, the complex architecture and the huge amount of parameters make the robust adjustment of DL models not easy, meanwhile it is not possible to generate all real-world corner cases for DL training.