STMD distills the full transition map of diffusion sampling SDEs into a conditional Mean Flow model to enable fast one- or few-step stochastic sampling without teacher models or bi-level optimization.
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FastUMAP speeds up UMAP by 15x on 70k-point datasets via bipartite landmark sampling and Nystrom initialization while retaining 96% of the kNN accuracy of stronger baselines.
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Stochastic Transition-Map Distillation for Fast Probabilistic Inference
STMD distills the full transition map of diffusion sampling SDEs into a conditional Mean Flow model to enable fast one- or few-step stochastic sampling without teacher models or bi-level optimization.
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FastUMAP: Scalable Dimensionality Reduction via Bipartite Landmark Sampling
FastUMAP speeds up UMAP by 15x on 70k-point datasets via bipartite landmark sampling and Nystrom initialization while retaining 96% of the kNN accuracy of stronger baselines.