Jeffrey Regier · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26733 · open-access PDF
神经后期估计 (NPE) 匹配一个被贬值的条件密度估计器到一个模拟的完整数据实现的训练集。NPE 的训练目标,日志概率,有利于将任意高密度分配给训练点的后期估计,并且被贬值的条件密度估计器的典型家庭(例如,正常化流量,高斯编码器)足够灵活,以便强制执行。由此产生的极端过度信心政策转化为保留数据。
Machine translation (Meta M2M100); the English original follows.
Neural Posterior Estimation (NPE) fits an amortized conditional density estimator to a simulated training set of complete-data realizations. NPE's training objective, log-likelihood, favors posterior approximations that assign arbitrarily high density to the training points, and typical families of amortized conditional density estimators (e.g., normalizing flows, Gaussian encoders) are flexible enough to oblige. The resulting policy of extreme overconfidence translates poorly to held-out data. Early stopping and other regularizers can prevent this degeneracy, but they are limited to selecting
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