3 papers
cs.LG2024
Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours
Yuxin Yang, Hongkuan Zhou, Rajgopal Kannan +1
Temporal Graph Neural Networks (TGNNs) have emerged as powerful tools for modeling dynamic interactions across various domains. The design space of TGNNs is notably complex, given…
cs.CV2024
Studying the Effects of Self-Attention on SAR Automatic Target Recognition
Jacob Fein-Ashley, Rajgopal Kannan, Viktor Prasanna
Attention mechanisms are critically important in the advancement of synthetic aperture radar (SAR) automatic target recognition (ATR) systems. Traditional SAR ATR models often stru…
cs.DC2018
GPOP: A cache- and work-efficient framework for Graph Processing Over Partitions
Kartik Lakhotia, Sourav Pati, Rajgopal Kannan +1
Past decade has seen the development of many shared-memory graph processing frameworks, intended to reduce the effort of developing high performance parallel applications. However…