用自适应退火缓解归一化流的模式坍缩问题
Mitigating mode collapse in normalizing flows by annealing with an adaptive schedule: Application to parameter estimation
- 基于有效样本量设计自适应退火策略，防止采样模式坍缩
- 在生化振荡器模型上，计算效率提升十倍于传统MCMC方法
- 可同时降低方差，适合高维复杂分布的参数估计场景
归一化流（NFs）能从复杂分布中生成不相关样本，是参数估计的有力工具。然而，其实际应用受限于易坍缩至多模分布单一模式的问题。本文提出基于有效样本量（ESS）的自适应退火策略，可有效缓解模式坍缩。实验表明，该方法在生化振荡器模型拟合时间序列数据时，边际似然收敛速度比广泛使用的集成马尔可夫链蒙特卡洛（MCMC）方法快十倍。此外，我们证明了ESS可用于剪枝样本以降低方差。这些进展对基于NF的采样具有普遍意义，并为未来改进提供了潜在方向。
原文摘要 · Abstract (English)
Normalizing flows (NFs) provide uncorrelated samples from complex distributions, making them an appealing tool for parameter estimation. However, the practical utility of NFs remains limited by their tendency to collapse to a single mode of a multimodal distribution. In this study, we show that annealing with an adaptive schedule based on the effective sample size (ESS) can mitigate mode collapse. We demonstrate that our approach can converge the marginal likelihood for a biochemical oscillator model fit to time-series data in ten-fold less computation time than a widely used ensemble Markov chain Monte Carlo (MCMC) method. We show that the ESS can also be used to reduce variance by pruning the samples. We expect these developments to be of general use for sampling with NFs and discuss potential opportunities for further improvements.
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