7 papers · 1 filter
Knowledge Homophily in Large Language Models
Utkarsh Sahu, Zhisheng Qi, Mahantesh Halappanavar +6
Large Language Models (LLMs) have been increasingly studied as neural knowledge bases for supporting knowledge-intensive applications such as question answering and fact checking.…
Towards High Resolution Probabilistic Coastal Inundation Forecasting from Sparse Observations
Kazi Ashik Islam, Zakaria Mehrab, Mahantesh Halappanavar +5
Coastal flooding poses increasing threats to communities worldwide, necessitating accurate and hyper-local inundation forecasting for effective emergency response. However, real-wo…
Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases
Yongjia Lei, Haoyu Han, Ryan A. Rossi +5
Text-rich Graph Knowledge Bases (TG-KBs) have become increasingly crucial for answering queries by providing textual and structural knowledge. However, current retrieval methods of…
SGS-GNN: A Supervised Graph Sparsification method for Graph Neural Networks
Siddhartha Shankar Das, Naheed Anjum Arafat, Muftiqur Rahman +3
We propose SGS-GNN, a novel supervised graph sparsifier that learns the sampling probability distribution of edges and samples sparse subgraphs of a user-specified size to reduce t…
Predictive Analytics of Varieties of Potatoes
Fabiana Ferracina, Bala Krishnamoorthy, Mahantesh Halappanavar +2
We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato clones in breeding trials by predicting their suitability f…
There is more to graphs than meets the eye: Learning universal features with self-supervision
Laya Das, Sai Munikoti, Nrushad Joshi +1
We study the problem of learning features through self-supervision that are generalisable to multiple graphs. State-of-the-art graph self-supervision restricts training to only one…