Many real-world dynamical systems can be described as State-Space Models (SSMs). In this formulation, each observation is emitted by a latent state, which follows first-order Markovian dynamics. A Probabilistic Deep SSM (ProDSSM) generalizes this framework to dynamical systems of unknown parametric form, where the transition and emission models are described by neural networks with uncertain weights. In this work, we propose the first deterministic inference algorithm for models of this type. Our framework allows efficient approximations for training and testing. We demonstrate in our experiments that our new method can be employed for a variety of tasks and enjoys a superior balance between predictive performance and computational budget.

翻译：许多实际动态系统可被描述为状态空间模型（SSM）。在该框架中，每个观测值由遵循一阶马尔可夫动力学的潜在状态生成。概率深度状态空间模型（ProDSSM）将此框架推广到参数形式未知的动态系统，其中转移模型和发射模型由具有不确定权重的神经网络描述。本文提出首个针对此类模型的确定性推理算法。我们的框架能够高效逼近训练与测试过程。实验表明，该新方法可适用于多种任务，并在预测性能与计算开销之间实现了更优的平衡。