论文标题
从限制性玻尔兹曼机器的隐藏层学习状态过渡规则
Learning State Transition Rules from Hidden Layers of Restricted Boltzmann Machines
论文作者
论文摘要
在许多科学和工程领域中，了解系统的动力很重要。可以通过使用机器学习技术从观察结果中学习状态过渡规则来解决此问题。这种观察到的时间序列数据通常由许多连续变量的序列组成，这些变量具有噪声和歧义性的，但是我们通常需要可以使用一些必需变量来建模的动态规则。在这项工作中，我们提出了一种从高维时序数据中提取少量必需的隐藏变量以及学习这些隐藏变量之间的状态过渡规则的方法。提出的方法基于受限的玻尔兹曼机器（RBM），该机器可在可见层中可观察到的可观察到的数据，并在隐藏层中处理潜在特征。但是，实际数据（例如视频和音频）包括离散变量和连续变量，这些变量具有时间关系。因此，我们提出了限制的Boltzmann机器（RTGB-RBM）的经常性颞叶，它结合了Gaussian-Bernoulli限制了Boltzmann机器（GB-RBM），以处理连续的可见变量，重复的时间限制性临时限制性限制性限制Boltzmann Machine（RT-RBM）之间的iNdivials（RT-RBM）之间的iNdive依赖性之间的iNdive Indive依赖性。我们还提出了一种基于规则的方法，该方法将基本信息提取为隐藏变量，并以可解释形式表示状态过渡规则。我们对弹跳球和移动MNIST数据集进行了实验，以评估我们提出的方法。实验结果表明，我们的方法可以学习这些物理系统的动态，因为隐藏变量之间的状态过渡规则，并且可以从观察到的状态转换中预测未观察到的未来状态。
Understanding the dynamics of a system is important in many scientific and engineering domains. This problem can be approached by learning state transition rules from observations using machine learning techniques. Such observed time-series data often consist of sequences of many continuous variables with noise and ambiguity, but we often need rules of dynamics that can be modeled with a few essential variables. In this work, we propose a method for extracting a small number of essential hidden variables from high-dimensional time-series data and for learning state transition rules between these hidden variables. The proposed method is based on the Restricted Boltzmann Machine (RBM), which treats observable data in the visible layer and latent features in the hidden layer. However, real-world data, such as video and audio, include both discrete and continuous variables, and these variables have temporal relationships. Therefore, we propose Recurrent Temporal GaussianBernoulli Restricted Boltzmann Machine (RTGB-RBM), which combines Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM) to handle continuous visible variables, and Recurrent Temporal Restricted Boltzmann Machine (RT-RBM) to capture time dependence between discrete hidden variables. We also propose a rule-based method that extracts essential information as hidden variables and represents state transition rules in interpretable form. We conduct experiments on Bouncing Ball and Moving MNIST datasets to evaluate our proposed method. Experimental results show that our method can learn the dynamics of those physical systems as state transition rules between hidden variables and can predict unobserved future states from observed state transitions.
