16 citations · 22 across the 27 of their papers we have counts for
11 papers · 1 filter
DEFT: Disentanglement-Enhanced Fine-Tuning for EEG Foundation Models
Yuqiu Deng, Muyun Jiang, Zongpeng Zhang +8
EEG foundation models (EFMs) have improved EEG representation learning by transferring knowledge from large-scale multi-subject pretraining to downstream tasks. However, their pret…
NeuroCogMap Reveals Cognitive Organization of Large Language Models
Zhongxiang Sun, Haolang Lu, Qiang Ma +11
Understanding how complex cognitive functions are organized within artificial systems is central to interpreting large language models (LLMs) and relating them to biological cognit…
ACPSL: Adaptive Communication-Computation Pipeline Parallel Split Learning over Edge Networks
Chenyu Liu, Zhaoyang Zhang, Zirui Chen +3
In wireless edge networks, split learning (SL) enables base station (BS) to utilize the distributed data and computing power across user equipments (UEs) to achieve collaborative m…
NüshuVoice: Reviving the Voice of Endangered Nüshu with Pitch-Aware Text-to-Speech
Hongkun Yang, Xinhui Yi, Xiyan Zhao +13
Nüshu is an endangered phonetic script historically used by women in Jiangyong County, southern Hunan, China. While existing computational studies of Nüshu mainly focus on textual…
InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs
Peiliang Gong, Emadeldeen Eldele, Chenyu Liu +8
Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting. However, existing methods predominantly rely on passive modality alignment…
DLink: Distilling Layer-wise and Dominant Knowledge from EEG Foundation Models
Jingyuan Wang, Zhihao Jia, Chenyu Liu +7
EEG foundation models (EFMs) achieve strong cross-subject and cross-task generalization through large-scale pretraining and downstream fine-tuning. Through empirical analysis, we o…
Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding
Peiliang Gong, Han Zhang, Zhen Jiang +5
Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled target domains without accessing…
A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery
Lufeng Feng, Baomin Xu, Haoran Zhang +7
Unilateral limb motor imagery (MI) plays an important role in upper-limb motor rehabilitation and precise control of external devices, and places higher demands on spatial resoluti…
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…
EEG-DLite: Dataset Distillation for Efficient Large EEG Model Training
Yuting Tang, Weibang Jiang, Shanglin Li +5
Large-scale EEG foundation models have shown strong generalization across a range of downstream tasks, but their training remains resource-intensive due to the volume and variable…
LoRAP: Low-Rank Aggregation Prompting for Quantized Graph Neural Networks Training
Chenyu Liu, Haige Li, Luca Rossi
Graph Neural Networks (GNNs) are neural networks that aim to process graph data, capturing the relationships and interactions between nodes using the message-passing mechanism. GNN…