做时间序列建模或状态空间模型研究的团队，PVMC让训练速度提升10倍且不牺牲精度，值得直接尝试。
深度状态空间模型（DSSM）在统计建模中广泛应用，但大规模训练一直困难。现有方法分为两类：自编码DSSM通过优化变分下界训练生成模型，而基于序贯蒙特卡洛（SMC）的方法可处理判别与生成任务，但因前向过程的顺序性在GPU上扩展性差。研究者提出并行变分蒙特卡洛（PVMC）方法，融合两类范式优势，在基准实验中达到或超越当前最优结果，且训练速度比最快的SMC方法快10倍。该方法可稳健训练DSSM用于判别和生成任务，解决了SMC方法在硬件上的扩展瓶颈。
Efficient Learning of Deep State Space Models via Importance Smoothing
Latent state space systems are ubiquitous in statistical modelling, arising naturally when a time series is observed through a noisy measurement function, however training deep state space models (DSSM) at scale remains difficult. Two largely distinct strategies and literatures have developed around the training of DSSMs. Firstly, auto-encoding DSSMs train generative DSSMs by optimising a variational lower bound. Secondly, DSSMs trained by back-propagating the outputs of a classical sequential Monte Carlo algorithm (SMC). Such approaches can train DSSMs for discriminative as well as generative tasks, however, due to the sequentiality of their forward pass, scale poorly on modern hardware. We propose a new training method \emph{parallel variational Monte Carlo} (PVMC) that bridges the gap between the paradigms, and can be used robustly to train DSSMs for both discriminative and generative tasks. Our method achieves state-of-the-art or better results on a set of baseline experiments and trains $10\times$ faster than the fastest competing SMC approach.