אימון מראש עם תרגומי גרפים

תקציר מקורי באנגליתarXiv:2609.13844v1 Announce Type: new Abstract: This article investigates pre-training strategies for graph transformers in the biochemistry domain. By conducting comprehensive experiments, the study reveals that supervised pre-training using computed properties as labels provides the highest performance gain on downstream tasks. The results also highlight the importance of constraining model capacity to mitigate overfitting in graph transformers.