most citedMaking Pre-trained Language Models Great on Tabular Prediction

2 citations · 2 across the 1 of their papers we have counts for

collaborators

6 papers

cs.CV2024

Multi-rater Prompting for Ambiguous Medical Image Segmentation

Jinhong Wang, Yi Cheng, Jintai Chen +3

Multi-rater annotations commonly occur when medical images are independently annotated by multiple experts (raters). In this paper, we tackle two challenges arisen in multi-rater a…

cs.LG2024

TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model

Yue Wang, Tianfan Fu, Yinlong Xu +6

Clinical trials are indispensable for medical research and the development of new treatments. However, clinical trials often involve thousands of participants and can span several…

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…

q-bio.BM2024

Generative AI for Controllable Protein Sequence Design: A Survey

Yiheng Zhu, Zitai Kong, Jialu Wu +6

The design of novel protein sequences with targeted functionalities underpins a central theme in protein engineering, impacting diverse fields such as drug discovery and enzymatic…

cs.LG2024

Multimodal Clinical Trial Outcome Prediction with Large Language Models

Wenhao Zheng, Liaoyaqi Wang, Dongshen Peng +5

The clinical trial is a pivotal and costly process, often spanning multiple years and requiring substantial financial resources. Therefore, the development of clinical trial outcom…

cs.CL2023

Mind's Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language Models

Weize Liu, Guocong Li, Kai Zhang +6

Large language models (LLMs) have achieved remarkable advancements in natural language processing. However, the massive scale and computational demands of these models present form…