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73 papers · 1 filter
Interpretable and Fair Generalized Additive Neural Networks via Multi-objective Learning
Ziming Wang, Changwu Huang, Ke Tang +2
Interpretability and fairness are two of the most emphasized dimensions in trustworthy artificial intelligence (AI). Various explainable AI methods have been introduced to improve…
Evidential Domain Adaptation for Remaining Useful Life Prediction with Incomplete Degradation
Yubo Hou, Mohamed Ragab, Yucheng Wang +5
Accurate Remaining Useful Life (RUL) prediction without labeled target domain data is a critical challenge, and domain adaptation (DA) has been widely adopted to address it by tran…
Uncover and Unlearn Nuisances: Agnostic Fully Test-Time Adaptation
Ponhvoan Srey, Yaxin Shi, Hangwei Qian +2
Fully Test-Time Adaptation (FTTA) addresses domain shifts without access to source data and training protocols of the pre-trained models. Traditional strategies that align source a…
Incremental Multistep Forecasting of Battery Degradation Using Pseudo Targets
Jonathan Adam Rico, Nagarajan Raghavan, Senthilnath Jayavelu
Data-driven models accurately perform early battery prognosis to prevent equipment failure and further safety hazards. Most existing machine learning (ML) models work in offline mo…
Intelligently Augmented Contrastive Tensor Factorization: Empowering Multi-dimensional Time Series Classification in Low-Data Environments
Anushiya Arunan, Yan Qin, Xiaoli Li +1
Classification of multi-dimensional time series from real-world systems require fine-grained learning of complex features such as cross-dimensional dependencies and intra-class var…
Compound Fault Diagnosis for Train Transmission Systems Using Deep Learning with Fourier-enhanced Representation
Jonathan Adam Rico, Nagarajan Raghavan, Senthilnath Jayavelu
Fault diagnosis prevents train disruptions by ensuring the stability and reliability of their transmission systems. Data-driven fault diagnosis models have several advantages over…