222 citations · 762 across the 46 of their papers we have counts for
50 papers
Scope is all you need: Transforming LLMs for HPC Code
Tal Kadosh, Niranjan Hasabnis, Vy A. Vo +9
With easier access to powerful compute resources, there is a growing trend in the field of AI for software development to develop larger and larger language models (LLMs) to addres…
Bias and Fairness in Large Language Models: A Survey
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow +6
Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into systems that touc…
Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation
Le Chen, Quazi Ishtiaque Mahmud, Hung Phan +2
Detecting parallelizable code regions is a challenging task, even for experienced developers. Numerous recent studies have explored the use of machine learning for code analysis an…
Neural Compositional Rule Learning for Knowledge Graph Reasoning
Kewei Cheng, Nesreen K. Ahmed, Yizhou Sun
Learning logical rules is critical to improving reasoning in KGs. This is due to their ability to provide logical and interpretable explanations when used for predictions, as well…
Causal Lifting and Link Prediction
Leonardo Cotta, Beatrice Bevilacqua, Nesreen Ahmed +1
Existing causal models for link prediction assume an underlying set of inherent node factors -- an innate characteristic defined at the node's birth -- that governs the causal evol…
Memory-Augmented Graph Neural Networks: A Brain-Inspired Review
Guixiang Ma, Vy A. Vo, Theodore Willke +1
We provide a comprehensive review of the existing literature on memory-augmented GNNs. We review these works through the lens of psychology and neuroscience, which has several esta…