Search Results for author:
Found 5 papers, 2 papers with code
4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs
Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably falls well behind advances in other domains such as computer vision or natural language processing.
OpenTab: Advancing Large Language Models as Open-domain Table Reasoners
Large Language Models (LLMs) trained on large volumes of data excel at various natural language tasks, but they cannot handle tasks requiring knowledge that has not been trained on previously.
Scaling Knowledge Graph Embedding Models
Developing scalable solutions for training Graph Neural Networks (GNNs) for link prediction tasks is challenging due to the high data dependencies which entail high computational cost and huge memory footprint.
BI-REC: Guided Data Analysis for Conversational Business Intelligence
In this paper, we propose BI-REC, a conversational recommendation system for BI applications to help users accomplish their data analysis tasks.
Medical Entity Disambiguation Using Graph Neural Networks
Entity disambiguation (also referred to as entity linking) is considered as an essential task in unlocking the wealth of such medical KBs.
