13 papers
Learning Label-Efficient Interpretable Medical Image Diagnosis via Semi-supervised Hypergraph Concept Bottleneck Model
Yijun Yang, Ruiqiang Xiao, Lijie Hu +4
Deep learning has revolutionized medical image analysis, delivering exceptional diagnostic accuracy across diverse applications. Yet, the lack of interpretability in its decision-m…
Matryoshka Concept Bottleneck Models
Ziye Chen, Hongbin Lin, Jie Li +1
Concept Bottleneck Models (CBMs) have emerged as a prominent paradigm for interpretable deep learning, learning by grounding predictions in human-understandable concepts. However,…
Multi-Adapter Representation Interventions via Energy Calibration
Manjiang Yu, Hongji Li, Junwei Chen +4
Representation intervention has emerged as a promising paradigm for aligning large language models toward desired behaviors without modifying model weights. Existing methods typica…
Understanding Generalization and Forgetting in In-Context Continual Learning
Guangyu Li, Meng Ding, Lijie Hu
In-context learning (ICL) derives its power from enabling Large Language Models to adapt to new tasks via prompt-based reasoning alone, entirely bypassing the need for parameter up…
Bayesian Gated Non-Negative Contrastive Learning
Peng Cui, Jiahao Zhang, Lijie Hu
While Contrastive Learning (CL) has revolutionized self-supervised representation learning, its latent representations remain highly entangled and opaque, limiting their interpreta…
MedFM-Robust: Benchmarking Robustness of Medical Foundation Models
Xiangxiang Cui, Tianjin Huang, Yifang Wang +2
Medical foundation models have achieved remarkable clinical performance, yet their robustness under real-world perturbations remains underexplored. We present a robustness benchmar…