activity
20222024
most citedExcelFormer: A neural network surpassing GBDTs on tabular data

9 citations · 25 across the 10 of their papers we have counts for

collaborators
Showing cs.LGShow all

6 papers · 1 filter

cs.LG2024★ 1 cited

Bridge-IF: Learning Inverse Protein Folding with Markov Bridges

Yiheng Zhu, Jialu Wu, Qiuyi Li +7

Inverse protein folding is a fundamental task in computational protein design, which aims to design protein sequences that fold into the desired backbone structures. While the deve…

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.LG2024★ 1 cited

Personalized Heart Disease Detection via ECG Digital Twin Generation

Yaojun Hu, Jintai Chen, Lianting Hu +5

Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention. Most traditional electrocardiogram (ECG) based…

cs.LG2023★ 6 cited

Sample-efficient Multi-objective Molecular Optimization with GFlowNets

Yiheng Zhu, Jialu Wu, Chaowen Hu +4

Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as a black-box optimization problem over the discrete chemical s…

cs.LG2023★ 9 cited

ExcelFormer: A neural network surpassing GBDTs on tabular data

Jintai Chen, Jiahuan Yan, Qiyuan Chen +3

Data organized in tabular format is ubiquitous in real-world applications, and users often craft tables with biased feature definitions and flexibly set prediction targets of their…

cs.LG2022★ 5 cited

T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction

Jiahuan Yan, Jintai Chen, Yixuan Wu +2

Recent development of deep neural networks (DNNs) for tabular learning has largely benefited from the capability of DNNs for automatic feature interaction. However, the heterogenei…