5 papers
A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes
Meiliang Liu, Huiwen Dong, Xiaoxiao Yang +6
With the advancement of deep learning technologies, various neural network-based Granger causality models have been proposed. Although these models have demonstrated notable improv…
An Interpretable Multi-Plane Fusion Framework With Kolmogorov-Arnold Network Guided Attention Enhancement for Alzheimer's Disease Diagnosis
Xiaoxiao Yang, Meiliang Liu, Yunfang Xu +4
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely impairs cognitive function and quality of life. Timely intervention in AD relies heavily on early…
SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis
Xinyue Yang, Meiliang Liu, Yunfang Xu +4
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that predominantly affects the elderly population and currently has no cure. Magnetic Resonance Imaging (MRI),…
Neuro-Informed Joint Learning Enhances Cognitive Workload Decoding in Portable BCIs
Xiaoxiao Yang, Chao Feng, Jiancheng Chen
Portable and wearable consumer-grade electroencephalography (EEG) devices, like Muse headbands, offer unprecedented mobility for daily brain-computer interface (BCI) applications,…
Kolmogorov-Arnold Networks for Time Series Granger Causality Inference
Meiliang Liu, Yunfang Xu, Zijin Li +4
We propose the Granger causality inference Kolmogorov-Arnold Networks (KANGCI), a novel architecture that extends the recently proposed Kolmogorov-Arnold Networks (KAN) to the doma…