Hypergraph Pre-training with Graph Neural Networks. - Boxin Du; Changhe Yuan; Robert A. Barton; Tal Neiman; Hanghang Tong (CoRR, 2021)

Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution. - Robert A. Barton; Tal Neiman; Changhe Yuan (IEEE Data Eng. Bull., 2021)

Self-supervised Hypergraph Representation Learning. - Boxin Du; Changhe Yuan; Robert A. Barton; Tal Neiman; Hanghang Tong (IEEE Big Data, 2022)

SystemMatch: optimizing preclinical drug models to human clinical outcomes via generative latent-space matching. - Scott Gigante; Varsha G. Raghavan; Amanda M. Robinson; Robert A. Barton; Adeeb H. Rahman; Drausin F. Wulsin; Jacques Banchereau; Noam Solomon; Luis F. Voloch; Fabian J. Theis (CoRR, 2022)

Enhancing Catalog Relationship Problems with Heterogeneous Graphs and Graph Neural Networks Distillation. - Boxin Du; Robert A. Barton; Grant Galloway; Junzhou Huang; Shioulin Sam; Ismail B. Tutar; Changhe Yuan (CIKM, 2023)

Neural Design for Genetic Perturbation Experiments. - Aldo Pacchiano; Drausin Wulsin; Robert A. Barton; Luis F. Voloch (ICLR, 2023)

Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information. - Yulun Wu; Robert A. Barton; Zichen Wang 002; Vassilis N. Ioannidis; Carlo De Donno; Layne C. Price; Luis F. Voloch; George Karypis (ICLR, 2023)

Enhancing Multimodal Large Language Models with Multi-instance Visual Prompt Generator for Visual Representation Enrichment. - Wenliang Zhong; Wenyi Wu; Qi Li; Robert A. Barton; Boxin Du; Shioulin Sam; Karim Bouyarmane; Ismail B. Tutar; Junzhou Huang (CoRR, 2024)