2 papers
quant-ph2025
QGHNN: A quantum graph Hamiltonian neural network
Wenxuan Wang
Representing and learning from graphs is essential for developing effective machine learning models tailored to non-Euclidean data. While Graph Neural Networks (GNNs) strive to add…
cs.DC2023
Multi-Path Bound for DAG Tasks
Qingqiang He, Nan Guan, Shuai Zhao +1
This paper studies the response time bound of a DAG (directed acyclic graph) task. Recently, the idea of using multiple paths to bound the response time of a DAG task, instead of u…