most citedMaking Pre-trained Language Models Great on Tabular Prediction

2 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Spatially Visual Perception for End-to-End Robotic Learning

Travis Davies, Jiahuan Yan, Xiang Chen +4

Recent advances in imitation learning have shown significant promise for robotic control and embodied intelligence. However, achieving robust generalization across diverse mounted…

cs.LG2024

Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPs

Jiahuan Yan, Jintai Chen, Qianxing Wang +2

Tabular datasets play a crucial role in various applications. Thus, developing efficient, effective, and widely compatible prediction algorithms for tabular data is important. Curr…

cs.CL20242 cited

Making Pre-trained Language Models Great on Tabular Prediction

Jiahuan Yan, Bo Zheng, Hongxia Xu +5

The transferability of deep neural networks (DNNs) has made significant progress in image and language processing. However, due to the heterogeneity among tables, such DNN bonus is…

cs.CL20231 cited

Battle of the Large Language Models: Dolly vs LLaMA vs Vicuna vs Guanaco vs Bard vs ChatGPT -- A Text-to-SQL Parsing Comparison

Shuo Sun, Yuchen Zhang, Jiahuan Yan +4

The success of ChatGPT has ignited an AI race, with researchers striving to develop new large language models (LLMs) that can match or surpass the language understanding and genera…

cs.CV20231 cited

GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels

Yixuan Wu, Jintai Chen, Jiahuan Yan +3

Since annotating medical images for segmentation tasks commonly incurs expensive costs, it is highly desirable to design an annotation-efficient method to alleviate the annotation…