1 citations · 2 across the 15 of their papers we have counts for
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NeurIPT: Foundation Model for Neural Interfaces
Zitao Fang, Chenxuan Li, Hongting Zhou +7
Electroencephalography (EEG) has wide-ranging applications, from clinical diagnosis to brain-computer interfaces (BCIs). With the increasing volume and variety of EEG data, there h…
Demystifying Deep Learning-based Brain Tumor Segmentation with 3D UNets and Explainable AI (XAI): A Comparative Analysis
Ming Jie Ong, Sze Yinn Ung, Sim Kuan Goh +1
The current study investigated the use of Explainable Artificial Intelligence (XAI) to improve the accuracy of brain tumor segmentation in MRI images, with the goal of assisting ph…
Sleep Stage Classification across Heterogeneous EEG/EOG Channels with Multi-Channel Differential Transformer
Benjamin Wei Hao Chin, Yuin Torng Yew, Haocheng Wu +6
Classification of sleep stages is essential for assessing sleep quality and diagnosing sleep disorders. However, manual inspection of EEG characteristics for each stage is time-con…
EEGDM: Label-Efficient EEG Representation Learning with Generative Diffusion Model
Jia Hong Puah, Sim Kuan Goh, Ziwei Zhang +6
Electroencephalography (EEG) is a critical tool for monitoring brain activity and diagnosing neurological disorders such as epilepsy. However, learning meaningful representations f…
Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer
Guodong Du, Zitao Fang, Jing Li +10
Foundation models and their checkpoints have significantly advanced deep learning, boosting performance across various applications. However, fine-tuned models often struggle outsi…
Multi-Modality Expansion and Retention for LLMs through Parameter Merging and Decoupling
Junlin Li, Guodong DU, Jing Li +8
Fine-tuning Large Language Models (LLMs) with multimodal encoders on modality-specific data expands the modalities that LLMs can handle, leading to the formation of Multimodal LLMs…
To See a World in a Spark of Neuron: Disentangling Multi-task Interference for Training-free Model Merging
Zitao Fang, Guodong DU, Shuyang Yu +6
Fine-tuning pre-trained models on targeted datasets enhances task-specific performance but often comes at the expense of generalization. Model merging techniques, which integrate m…