论文标题
线性变分空间过滤
Linear Variational State-Space Filtering
论文作者
论文摘要
我们介绍了各种状态空间过滤器（VSSF），这是一种无监督学习，识别和过滤潜在的马尔可夫状态空间模型的新方法。我们为在异质传感器配置下提供了一个理论上的声音框架，用于潜在状态空间推断。最终的模型可以整合训练过程中使用的传感器测量值的任意子集，从而使学习半监督状态表示形式学习，从而强制实施学到的潜在状态空间的某些组成部分，以同意可解释的测量。从这个框架中，我们得出了L-VSSF，这是该模型具有线性潜在动力学和高斯分布参数化的明确实例化。我们在实验上证明了L-VSSF在几个不同测试环境中训练数据集的序列长度之外的潜在空间中过滤的能力。
We introduce Variational State-Space Filters (VSSF), a new method for unsupervised learning, identification, and filtering of latent Markov state space models from raw pixels. We present a theoretically sound framework for latent state space inference under heterogeneous sensor configurations. The resulting model can integrate an arbitrary subset of the sensor measurements used during training, enabling the learning of semi-supervised state representations, thus enforcing that certain components of the learned latent state space to agree with interpretable measurements. From this framework we derive L-VSSF, an explicit instantiation of this model with linear latent dynamics and Gaussian distribution parameterizations. We experimentally demonstrate L-VSSF's ability to filter in latent space beyond the sequence length of the training dataset across several different test environments.
