5 citations · 11 across the 6 of their papers we have counts for
6 papers
Beyond the Vision Encoder: Identifying and Mitigating Spatial Bias in Large Vision-Language Models
Yingjie Zhu, Xuefeng Bai, Kehai Chen +6
Large Vision-Language Models (LVLMs) have achieved remarkable success across a wide range of multimodal tasks, yet their robustness to spatial variations remains insufficiently und…
Evaluating and Steering Modality Preferences in Multimodal Large Language Model
Yu Zhang, Jinlong Ma, Yongshuai Hou +5
Multi-modal large language models (MLLMs) have achieved remarkable success on complex multi-modal tasks. However, it remains insufficiently explored whether they exhibit $\textbf{m…
SHAPE: A Sample-adaptive Hierarchical Prediction Network for Medication Recommendation
Sicen Liu, Xiaolong Wang, JIngcheng Du +6
Effectively medication recommendation with complex multimorbidity conditions is a critical task in healthcare. Most existing works predicted medications based on longitudinal recor…
Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters
Yang Xiang, Zhihua Wu, Weibao Gong +15
The ever-growing model size and scale of compute have attracted increasing interests in training deep learning models over multiple nodes. However, when it comes to training on clo…
Multimodal data matters: language model pre-training over structured and unstructured electronic health records
Sicen Liu, Xiaolong Wang, Yongshuai Hou +5
As two important textual modalities in electronic health records (EHR), both structured data (clinical codes) and unstructured data (clinical narratives) have recently been increas…
Machine Learning for Multimodal Electronic Health Records-based Research: Challenges and Perspectives
Ziyi Liu, Jiaqi Zhang, Yongshuai Hou +3
Background: Electronic Health Records (EHRs) contain rich information of patients' health history, which usually include both structured and unstructured data. There have been many…