We present Neural Adaptive Smoothing via Twisting (NAS-X), a method for learning and inference in sequential latent variable models based on reweighted wake-sleep (RWS). NAS-X works with both discrete and continuous latent variables, and leverages smoothing SMC to fit a broader range of models than traditional RWS methods. We test NAS-X on discrete and continuous tasks and find that it substantially outperforms previous variational and RWS-based methods in inference and parameter recovery.

翻译：我们提出通过扭曲实现神经自适应平滑（NAS-X），这是一种基于重加权唤醒-睡眠（RWS）的序贯潜变量模型学习与推断方法。NAS-X可同时处理离散和连续潜变量，并利用平滑序贯蒙特卡洛方法拟合比传统RWS方法更广泛的模型。我们在离散和连续任务上测试NAS-X，发现其在推断和参数恢复方面显著优于先前基于变分和RWS的方法。