3 accepted papers
The most recent approaches for clustering single-cell RNA-sequencing data rely on deep auto-encoders. However, three major challenges remain unaddressed. First, current models overlook the impact of the cumulative errors induced by the pseudo-supervised embedding clustering task (Feature Randomness)…
The deep clustering paradigm has shown great potential for discovering complex patterns that can reveal cell heterogeneity in single-cell RNA sequencing data. This paradigm involves two training phases: pretraining based on a pretext task and fine-tuning using pseudo-labels. Although current models…
Most recent graph clustering methods rely on pretraining graph auto-encoders using self-supervision techniques (pretext task) and finetuning based on pseudo-supervision (main task). However, the transition from self-supervision to pseudo-supervision has never been studied from a geometric perspectiv…