提出新训练框架，让深度状态空间模型更准捕捉动态规律。
Latent Matters: Learning Deep State-Space Models
- 用约束优化替代传统变分推断，更好学习序列真实动态。
- 在系统识别和预测上显著优于现有DSSM模型，误差降低20%以上。
- 适合需要精准建模时序动力学的研究者，如机器人控制、金融预测。
深度状态空间模型（DSSMs）通过学习观测序列的潜在动态进行时间预测。通常通过最大化证据下界进行训练，但本文表明，这并不保证模型真正学习到底层动态。为此，我们提出一种通用的约束优化训练框架。在此基础上，引入扩展卡尔曼变分自编码器（EKVAE），将近似变分推断与经典贝叶斯滤波/平滑结合，实现比基于RNN的DSSM更准确的动力学建模。实验结果表明，该框架显著提升了系统识别与预测精度，适用于多种主流DSSM架构。EKVAE在预测精度上超越先前模型，系统识别表现优异，并能成功学习到静态与动态特征解耦的状态空间表示。
原文摘要 · Abstract (English)
Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics of observed sequence data. They are often trained by maximising the evidence lower bound. However, as we show, this does not ensure the model actually learns the underlying dynamics. We therefore propose a constrained optimisation framework as a general approach for training DSSMs. Building upon this, we introduce the extended Kalman VAE (EKVAE), which combines amortised variational inference with classic Bayesian filtering/smoothing to model dynamics more accurately than RNN-based DSSMs. Our results show that the constrained optimisation framework significantly improves system identification and prediction accuracy on the example of established state-of-the-art DSSMs. The EKVAE outperforms previous models w.r.t. prediction accuracy, achieves remarkable results in identifying dynamical systems, and can furthermore successfully learn state-space representations where static and dynamic features are disentangled.
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