no code implementations • 13 Mar 2024 • Zhuoxin Chen, Zhenyu Wu, Yang Ji
In the second stage, DFL-FS employs federated feature regeneration based on global feature statistics and utilizes resampling and weighted covariance to calibrate the global classifier to enhance the model's adaptability to long-tailed data distributions.
no code implementations • 21 Aug 2023 • Xiaona Sun, Zhenyu Wu, Yichen Liu, Saier Hu, ZhiQiang Zhan, Yang Ji
Unsupervised Domain Adaptation (UDA) approaches address the covariate shift problem by minimizing the distribution discrepancy between the source and target domains, assuming that the label distribution is invariant across domains.
no code implementations • 16 Aug 2023 • Jialin Guo, Zhenyu Wu, ZhiQiang Zhan, Yang Ji
Moreover, we noticed that the traditional calibration evaluation metric, Excepted Calibration Error (ECE), gives a higher weight to low-confidence samples in the minority classes, which leads to inaccurate evaluation of model calibration.
1 code implementation • 10 Aug 2021 • Moyu Zhang, Xinning Zhu, Chunhong Zhang, Yang Ji, Feng Pan, Changchuan Yin
In this paper, we propose Multi-Factors Aware Dual-Attentional model (MF-DAKT) which enriches question representations and utilizes multiple factors to model students' learning progress based on a dual-attentional mechanism.
no code implementations • 19 Nov 2018 • Wenfang Lin, Zhen-Yu Wu, Yang Ji
Data-driven fault diagnostics and prognostics suffers from class-imbalance problem in industrial systems and it raises challenges to common machine learning algorithms as it becomes difficult to learn the features of the minority class samples.