26 citations · 62 across the 18 of their papers we have counts for
6 papers · 1 filter
AIM: Adaptive Intra-Network Modulation for Balanced Multimodal Learning
Shu Shen, C. L. Philip Chen, Tong Zhang
Multimodal learning has significantly enhanced machine learning performance but still faces numerous challenges and limitations. Imbalanced multimodal learning is one of the proble…
CRIA: A Cross-View Interaction and Instance-Adapted Pre-training Framework for Generalizable EEG Representations
Puchun Liu, C. L. Philip Chen, Yubin He +1
The difficulty of extracting deep features from EEG data and effectively integrating information from multiple views presents significant challenges for developing a generalizable…
FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition
Haiqi Liu, C. L. Philip Chen, Tong Zhang
Cross-subject EEG emotion recognition is challenged by significant inter-subject variability and intricately entangled intra-subject variability. Existing works have primarily addr…
Reliable Multimodal Learning Via Multi-Level Adaptive DeConfusion
Tong Zhang, Shu Shen, C. L. Philip Chen
Multimodal learning enhances the performance of various machine learning tasks by leveraging complementary information across different modalities. However, existing methods often…
DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning
Jiaxin Guo, C. L. Philip Chen, Shuzhen Li +1
Cold-start active learning (CSAL) selects valuable instances from an unlabeled dataset for manual annotation. It provides high-quality data at a low annotation cost for label-scarc…
Online-BLS: An Accurate and Efficient Online Broad Learning System for Data Stream Classification
Chunyu Lei, Guang-Ze Chen, C. L. Philip Chen +1
The state-of-the-art online learning models generally conduct a single online gradient descent when a new sample arrives and thus suffer from suboptimal model weights. To this end,…