Weighted InfoNCE objectives realize specific target geometries in embedding space, with SupCon producing size-dependent inter-class similarities under imbalance while Soft SupCon and certain continuous variants preserve regular simplex or unique optima.
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FastUMAP approximates UMAP via sparse bipartite point-landmark graphs and Nystrom initialization to deliver lower runtimes than Barnes-Hut t-SNE on most tested datasets while retaining competitive kNN accuracy.
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A Unified Geometric Framework for Weighted Contrastive Learning
Weighted InfoNCE objectives realize specific target geometries in embedding space, with SupCon producing size-dependent inter-class similarities under imbalance while Soft SupCon and certain continuous variants preserve regular simplex or unique optima.
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FastUMAP: Scalable Dimensionality Reduction via Bipartite Landmark Sampling
FastUMAP approximates UMAP via sparse bipartite point-landmark graphs and Nystrom initialization to deliver lower runtimes than Barnes-Hut t-SNE on most tested datasets while retaining competitive kNN accuracy.