5 citations · 9 across the 6 of their papers we have counts for
6 papers
TTM-RE: Memory-Augmented Document-Level Relation Extraction
Chufan Gao, Xuan Wang, Jimeng Sun
Document-level relation extraction aims to categorize the association between any two entities within a document. We find that previous methods for document-level relation extracti…
Signal Quality Auditing for Time-series Data
Chufan Gao, Nicholas Gisolfi, Artur Dubrawski
Signal quality assessment (SQA) is required for monitoring the reliability of data acquisition systems, especially in AI-driven Predictive Maintenance (PMx) application contexts. S…
PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming
Chufan Gao, Xulin Fan, Jimeng Sun +1
Relation extraction aims to classify the relationships between two entities into pre-defined categories. While previous research has mainly focused on sentence-level relation extra…
DRG-LLaMA : Tuning LLaMA Model to Predict Diagnosis-related Group for Hospitalized Patients
Hanyin Wang, Chufan Gao, Christopher Dantona +2
In the U.S. inpatient payment system, the Diagnosis-Related Group (DRG) is pivotal, but its assignment process is inefficient. The study introduces DRG-LLaMA, an advanced large lan…
Classifying Unstructured Clinical Notes via Automatic Weak Supervision
Chufan Gao, Mononito Goswami, Jieshi Chen +1
Healthcare providers usually record detailed notes of the clinical care delivered to each patient for clinical, research, and billing purposes. Due to the unstructured nature of th…
Learning Graph Neural Networks for Multivariate Time Series Anomaly Detection
Saswati Ray, Sana Lakdawala, Mononito Goswami +1
In this work, we propose GLUE (Graph Deviation Network with Local Uncertainty Estimation), building on the recently proposed Graph Deviation Network (GDN). GLUE not only automatica…