most citedResidual-based Language Models are Free Boosters for Biomedical Imaging

8 citations · 15 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20241 cited

Deep Representation Learning for Multi-functional Degradation Modeling of Community-dwelling Aging Population

Suiyao Chen, Xinyi Liu, Yulei Li +2

As the aging population grows, particularly for the baby boomer generation, the United States is witnessing a significant increase in the elderly population experiencing multifunct…

cs.LG20241 cited

The New Agronomists: Language Models are Experts in Crop Management

Jing Wu, Zhixin Lai, Suiyao Chen +3

Crop management plays a crucial role in determining crop yield, economic profitability, and environmental sustainability. Despite the availability of management guidelines, optimiz…

cs.CV20248 cited

Residual-based Language Models are Free Boosters for Biomedical Imaging

Zhixin Lai, Jing Wu, Suiyao Chen +2

In this study, we uncover the unexpected efficacy of residual-based large language models (LLMs) as part of encoders for biomedical imaging tasks, a domain traditionally devoid of…

cs.LG20241 cited

Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detection

Zhixin Lai, Xuesheng Zhang, Suiyao Chen

Large language models (LLMs) have reached human-like proficiency in generating diverse textual content, underscoring the necessity for effective fake text detection to avoid potent…

cs.LG20244 cited

SwitchTab: Switched Autoencoders Are Effective Tabular Learners

Jing Wu, Suiyao Chen, Qi Zhao +9

Self-supervised representation learning methods have achieved significant success in computer vision and natural language processing, where data samples exhibit explicit spatial or…