1 citations · 4 across the 12 of their papers we have counts for
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SCOPE: Structured Prototype-Guided Adaptation for EEG Foundation Models with Limited Labels
Jingying Ma, Feng Wu, Yucheng Xing +5
Electroencephalography (EEG) foundation models (EFMs) have shown strong potential for transferable representation learning, yet their adaptation in realistic settings remains chall…
CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model
Jingying Ma, Feng Wu, Qika Lin +4
Electroencephalography (EEG) provides real-time insights into brain activity and supports diverse applications in neuroscience. While EEG foundation models (EFMs) have emerged to a…
A Survey of Quantized Graph Representation Learning: Connecting Graph Structures with Large Language Models
Qika Lin, Zhen Peng, Kaize Shi +5
Recent years have witnessed rapid advances in graph representation learning, with the continuous embedding approach emerging as the dominant paradigm. However, such methods encount…
Development and Validation of a Dynamic Kidney Failure Prediction Model based on Deep Learning: A Real-World Study with External Validation
Jingying Ma, Jinwei Wang, Lanlan Lu +7
Background: Chronic kidney disease (CKD), a progressive disease with high morbidity and mortality, has become a significant global public health problem. Most existing models are s…