In this paper, we study the role of machine-learned predictions inofflineNP-hard problems. For offline problems, an algorithm has no information disadvantage compared to an optimal solution: thedisadvantage iscomputational.

Cardinal data are numerical scores.

Byutilizingmodality dropout during pre-training, we demonstrate that a single fine-tuned model can achieve performance on par or better than the state-of-the-art modality-specific models.

Graph Structure Learning (GSL), a family of data-centric learning approaches, has garnered substantial attention in recent years. The core concept behind GSL is to jointly optimize the graph structure and the corresponding GNN models.

However, the lack of expert-annotated, open-access resources has hindered the analysis of ALS data using advanced deep learning techniques.