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Sep, 2023
无采样概率深度状态空间模型
Sampling-Free Probabilistic Deep State-Space Models
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Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters
TL;DR
提出了一种基于神经网络的确定性推理算法，用于训练和测试具有未知参数形式的概率深度状态空间模型，实验结果表明该方法在预测性能和计算预算方面具有卓越的平衡性。
Abstract
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
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