11 citations · 14 across the 4 of their papers we have counts for
4 papers
CONE: Embeddings for Complex Numerical Data Preserving Unit and Variable Semantics
Gyanendra Shrestha, Anna Pyayt, Michael Gubanov
Large pre-trained models (LMs) and Large Language Models (LLMs) are typically effective at capturing language semantics and contextual relationships. However, these models encounte…
Tabular Embeddings for Tables with Bi-Dimensional Hierarchical Metadata and Nesting
Gyanendra Shrestha, Chutain Jiang, Sai Akula +3
Embeddings serve as condensed vector representations for real-world entities, finding applications in Natural Language Processing (NLP), Computer Vision, and Data Management across…
CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care
Michael Gubanov, Anna Pyayt, Aleksandra Karolak
Here, we describe one of the first Web-scale hybrid Knowledge Graph (KG)-Large Language Model (LLM), populated with the latest peer-reviewed medical knowledge on colorectal Cancer.…
BigDB: Automatic Machine Learning Optimizer
Anna Pyayt, Michael Gubanov
In this short vision paper, we introduce a machine learning optimizer for data management and describe its architecture and main functionality.