Prioritizing test cases for deep learning-based video classifiers
The widespread adoption of video-based applications across various fields highlights their
importance in modern software systems. However, in comparison to images or text, labelling …
importance in modern software systems. However, in comparison to images or text, labelling …
Towards exploring the limitations of test selection techniques on graph neural networks: An empirical study
Abstract Graph Neural Networks (GNNs) have gained prominence in various domains, such
as social network analysis, recommendation systems, and drug discovery, due to their ability …
as social network analysis, recommendation systems, and drug discovery, due to their ability …
An empirical study of AI techniques in mobile applications
The integration of artificial intelligence (AI) into mobile applications has significantly
transformed various domains, enhancing user experiences and providing personalized …
transformed various domains, enhancing user experiences and providing personalized …
Test Input Prioritization for Graph Neural Networks
GNNs have shown remarkable performance in a variety of classification tasks. The reliability
of GNN models needs to be thoroughly validated before their deployment to ensure their …
of GNN models needs to be thoroughly validated before their deployment to ensure their …
[PDF][PDF] Test Input Prioritization for Deep Neural Networks
Y LI - 2024 - orbilu.uni.lu
The rapid adoption of deep neural networks (DNNs) has revolutionized machine learning in
several domains. As a result, thorough evaluation and validation of DNNs are crucial for …
several domains. As a result, thorough evaluation and validation of DNNs are crucial for …