1 citations · 1 across the 9 of their papers we have counts for
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Regularizing modality contribution drift in multimodal continual learning
Zhen Zhang, Jielei Chu, Bin Liu +1
Multimodal continual learning (MMCL) aims to learn emerging knowledge from multimodal data while preserving knowledge. To mitigate forgetting, current MMCL methods usually focus on…
Boundary Embedding Shaping with Adaptive Contrastive Learning for Graph Structural Disentanglement
Jiaqing Chen, Zidu Yin, Yichao Cai +4
Graph neural networks (GNNs) excel at aggregating neighbor information for classification, yet their performance is hindered by graph structural entanglement, where spurious correl…
Causally Sufficient and Necessary Feature Expansion for Class-Incremental Learning
Zhen Zhang, Jielei Chu, Jiangtao Hu +4
Current expansion-based methods for Class Incremental Learning (CIL) effectively mitigate catastrophic forgetting by freezing old features. However, such task-specific features lea…
The Geometric Mechanics of Contrastive Representation Learning: Alignment Potentials, Entropic Dispersion, and Cross-modal Divergence
Yichao Cai, Zhen Zhang, Yuhang Liu +1
While InfoNCE underlies modern contrastive learning, its geometric mechanisms remain under-characterized beyond the canonical alignment--uniformity decomposition. We develop a meas…
On the Value of Cross-Modal Misalignment in Multimodal Representation Learning
Yichao Cai, Yuhang Liu, Erdun Gao +4
Multimodal representation learning, exemplified by multimodal contrastive learning (MMCL) using image-text pairs, aims to learn powerful representations by aligning cues across mod…
Analytic DAG Constraints for Differentiable DAG Learning
Zhen Zhang, Ignavier Ng, Dong Gong +6
Recovering the underlying Directed Acyclic Graph (DAG) structures from observational data presents a formidable challenge, partly due to the combinatorial nature of the DAG-constra…