most citedBridge the Gap between Language models and Tabular Understanding

6 citations · 8 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2023

Large Language Models Are Partially Primed in Pronoun Interpretation

Suet-Ying Lam, Qingcheng Zeng, Kexun Zhang +2

While a large body of literature suggests that large language models (LLMs) acquire rich linguistic representations, little is known about whether they adapt to linguistic biases i…

cs.CV2023

Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation

Xiangyi Yan, Junayed Naushad, Chenyu You +6

Recent advancements in self-supervised learning have demonstrated that effective visual representations can be learned from unlabeled images. This has led to increased interest in…

cs.CL20236 cited

Bridge the Gap between Language models and Tabular Understanding

Nuo Chen, Linjun Shou, Ming Gong +5

Table pretrain-then-finetune paradigm has been proposed and employed at a rapid pace after the success of pre-training in the natural language domain. Despite the promising finding…

eess.IV2022

Learning correspondences of cardiac motion from images using biomechanics-informed modeling

Xiaoran Zhang, Chenyu You, Shawn Ahn +3

Learning spatial-temporal correspondences in cardiac motion from images is important for understanding the underlying dynamics of cardiac anatomical structures. Many methods explic…

cs.CL2022

Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension

Nuo Chen, Chenyu You

Recently, the attention-enhanced multi-layer encoder, such as Transformer, has been extensively studied in Machine Reading Comprehension (MRC). To predict the answer, it is common…

cs.CV20221 cited

Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation

Chenyu You, Jinlin Xiang, Kun Su +5

Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model th…