Self-Supervised Pretraining of Molecular Graph Encoders with LeJEPA
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arXiv:2609.04261v1 Announce Type: cross Abstract: Self-supervised pretraining has transformed language and vision, but its value for molecular graph neural networks remains contested. We ask whether pretraining on a large unlabelled corpus improves molecular property prediction. We adapt LeJEPA, a…
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- 2026-09-07 04:00 · arXiv stat.ML
Self-Supervised Pretraining of Molecular Graph Encoders with LeJEPA