AIBullisharXiv – CS AI · 9h ago7/10
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It Just Takes Two: Scaling Amortized Inference to Large Sets
Researchers introduce a novel training strategy for neural posterior estimation that decouples representation learning from posterior modeling, enabling amortized inference on large observation sets by training only on pairs of examples. The approach dramatically reduces computational requirements while maintaining or improving performance across diverse benchmarks, making scalable Bayesian inference practical for real-world applications.