论文标题
从高维噪声数据中对动态模型的深入学习
Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data
论文作者
论文摘要
这项工作提出了一种从高维嘈杂数据中数据驱动的低维动力学模型的随机变化深内核学习方法。该框架由一个编码器组成，该编码器将高维测量值压缩到低维状态变量中，以及对状态变量的潜在动力学模型，该模型可以预测随着时间的推移系统演变。提出的模型的训练是以无监督的方式进行的，即不依赖标记的数据。我们的学习方法是根据摆锤的运动进行评估的，这是通过高维嘈杂的RGB图像测量的非线性模型识别和对照的精心研究的基线。结果表明，该方法可以有效地确定测量，学习紧凑的状态表示和潜在的动力学模型，并识别和量化建模不确定性。
This work proposes a Stochastic Variational Deep Kernel Learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data. The framework is composed of an encoder that compresses high-dimensional measurements into low-dimensional state variables, and a latent dynamical model for the state variables that predicts the system evolution over time. The training of the proposed model is carried out in an unsupervised manner, i.e., not relying on labeled data. Our learning method is evaluated on the motion of a pendulum -- a well studied baseline for nonlinear model identification and control with continuous states and control inputs -- measured via high-dimensional noisy RGB images. Results show that the method can effectively denoise measurements, learn compact state representations and latent dynamical models, as well as identify and quantify modeling uncertainties.
