1 citations · 2 across the 5 of their papers we have counts for
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Predicting Multi-View Rashomon Representation: Can We Learn Where Models Disagree?
Mingyue Ma, Zongbo Han, Changqing Zhang +1
Foundation models are increasingly adopted across a wide range of applications, often serving as core blocks within AI systems. Yet different foundation models may encode the same…
FAR-DPO: Feasibility-Aware and Robust Direct Preference Optimization for Cyclic Peptide Design
Guofeng Zhang, Rong Han, Xiaoyu Wang +4
Cyclic peptides are emerging as promising molecular scaffolds in drug discovery due to their high binding affinity and structural stability. However, extending generative models fr…
Fusion Anything: A Generalized Multimodal Foundation Model
Huizi Cui, Zongbo Han, Chenggong Ding +6
Making prediction with multimodal data is widely used in diverse scenarios. Existing multimodal fusion models, once deployed, can only handle predefined modalities (e.g., vision, t…
DOTA: Distributional Test-Time Adaptation of Vision-Language Models
Zongbo Han, Jialong Yang, Guangyu Wang +4
Vision-language foundation models (VLMs), such as CLIP, exhibit remarkable performance across a wide range of tasks. However, deploying these models can be unreliable when signific…
Learning with Noisy Labels over Imbalanced Subpopulations
MingCai Chen, Yu Zhao, Bing He +3
Learning with Noisy Labels (LNL) has attracted significant attention from the research community. Many recent LNL methods rely on the assumption that clean samples tend to have "sm…