1 citations · 2 across the 15 of their papers we have counts for
16 papers
STEPS: A Temporal Smooth Error Propagation Solver on the Manifolds for Test-Time Adaptation in Time Series Forecasting
Jiaqi Liu, Yifan Ouyang, Zhifei Song +2
Test-Time Adaptation (TTA) aims to improve time series forecasting under distribution shifts by using limited observations revealed during inference. However, forecasting TTA must…
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…
Parameter Competition Balancing for Model Merging
Guodong Du, Junlin Lee, Jing Li +8
While fine-tuning pretrained models has become common practice, these models often underperform outside their specific domains. Recently developed model merging techniques enable t…
Meta-heuristic Optimizer Inspired by the Philosophy of Yi Jing
Yisheng Yang, Sim Kuan Goh, Qing Cai +2
Drawing inspiration from the philosophy of Yi Jing, the Yin-Yang pair optimization (YYPO) algorithm has been shown to achieve competitive performance in single objective optimizati…
Impacts of Darwinian Evolution on Pre-trained Deep Neural Networks
Guodong Du, Runhua Jiang, Senqiao Yang +5
Darwinian evolution of the biological brain is documented through multiple lines of evidence, although the modes of evolutionary changes remain unclear. Drawing inspiration from th…
Evolutionary Neural Architecture Search for 3D Point Cloud Analysis
Yisheng Yang, Guodong Du, Chean Khim Toa +2
Neural architecture search (NAS) automates neural network design by using optimization algorithms to navigate architecture spaces, reducing the burden of manual architecture design…
Knowledge Fusion By Evolving Weights of Language Models
Guodong Du, Jing Li, Hanting Liu +5
Fine-tuning pre-trained language models, particularly large language models, demands extensive computing resources and can result in varying performance outcomes across different d…
CADE: Cosine Annealing Differential Evolution for Spiking Neural Network
Runhua Jiang, Guodong Du, Shuyang Yu +3
Spiking neural networks (SNNs) have gained prominence for their potential in neuromorphic computing and energy-efficient artificial intelligence, yet optimizing them remains a form…
A Novel Non-population-based Meta-heuristic Optimizer Inspired by the Philosophy of Yi Jing
Ho-Kin Tang, Sim Kuan Goh
Drawing inspiration from the philosophy of Yi Jing, Yin-Yang pair optimization (YYPO) has been shown to achieve competitive performance in single objective optimizations. Besides,…
A Tunnel Gaussian Process Model for Learning Interpretable Flight's Landing Parameters
Sim Kuan Goh, Narendra Pratap Singh, Zhi Jun Lim +1
Approach and landing accidents have resulted in a significant number of hull losses worldwide. Technologies (e.g., instrument landing system) and procedures (e.g., stabilized appro…