6 papers · 1 filter
Clinical Trajectory Alignment for Medical Vision-Language Pre-training
Huimin Yan, Xian Yang, Zhi Wang +1
Medical vision-language pre-training largely follows a visit-level image-report matching paradigm, aligning paired images and reports at individual visits. While effective for stat…
Rethinking Generalization in Graph Neural Networks: A Structural Complexity Perspective
Peiyao Wang, Liang Bai, Xian Yang +2
Graph neural networks (GNNs) have emerged as a fundamental tool for learning from graph-structured data, achieving strong performance across a wide range of applications. However,…
Exploring the Impact of Parameter Update Magnitude on Forgetting and Generalization of Continual Learning
JinLi He, Liang Bai, Xian Yang
The magnitude of parameter updates are considered a key factor in continual learning. However, most existing studies focus on designing diverse update strategies, while a theoretic…
Understanding the Role of Rehearsal Scale in Continual Learning under Varying Model Capacities
JinLi He, Liang Bai, Xian Yang
Rehearsal is one of the key techniques for mitigating catastrophic forgetting and has been widely adopted in continual learning algorithms due to its simplicity and practicality. H…
LLM-Guided Diagnostic Evidence Alignment for Medical Vision-Language Pretraining under Limited Pairing
Huimin Yan, Liang Bai, Xian Yang +1
Most existing CLIP-style medical vision--language pretraining methods rely on global or local alignment with substantial paired data. However, global alignment is easily dominated…
Bipartite Graph Attention-based Clustering for Large-scale scRNA-seq Data
Zhuomin Liang, Liang Bai, Xian Yang
scRNA-seq clustering is a critical task for analyzing single-cell RNA sequencing (scRNA-seq) data, as it groups cells with similar gene expression profiles. Transformers, as powerf…