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GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection
In this paper we present GraSS, a novel approach for automatic SAT solver selection based on tripartite graph representations of instances and a heterogeneous graph neural network (GNN) model.
Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification
Multi-label text classification (MLTC) is the task of assigning multiple labels to a given text, and has a wide range of application domains.
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