RePercENT introduces a plug-and-play self-supervised framework for scalable pairwise disentangled representation learning across more than two modalities using pre-extracted embeddings and a joint optimization objective with theoretical optimality guarantees.
Senmo: A self-normalizing deep learning model for enhanced multi-omics data analysis in oncology.arXiv preprint arXiv:2405.08226,
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RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities
RePercENT introduces a plug-and-play self-supervised framework for scalable pairwise disentangled representation learning across more than two modalities using pre-extracted embeddings and a joint optimization objective with theoretical optimality guarantees.