论文标题
信息丰富的神经合奏Kalman学习
Informative Neural Ensemble Kalman Learning
论文作者
论文摘要
在随机系统中，信息性方法选择了关键测量或决策变量，以最大程度地提高信息增益，以增强与模型相关推论的疗效。神经学习还体现了随机动力学，但是信息性学习的发展较少。在这里，我们提出了信息丰富的合奏Kalman学习，该学习用自适应的集合滤波器代替了反向传播，以量化不确定性，并在学习过程中最大程度地提高信息增益。在展示了合奏Kalman Learning在标准数据集上的竞争性能之后，我们将信息性方法应用于神经结构学习。特别是，我们表明，当从Lorenz-63系统的模拟中训练时，有效学习的结构会恢复动力学方程。据我们所知，内容丰富的合奏Kalman学习是新的。结果表明，这种优化学习方法是有希望的。
In stochastic systems, informative approaches select key measurement or decision variables that maximize information gain to enhance the efficacy of model-related inferences. Neural Learning also embodies stochastic dynamics, but informative Learning is less developed. Here, we propose Informative Ensemble Kalman Learning, which replaces backpropagation with an adaptive Ensemble Kalman Filter to quantify uncertainty and enables maximizing information gain during Learning. After demonstrating Ensemble Kalman Learning's competitive performance on standard datasets, we apply the informative approach to neural structure learning. In particular, we show that when trained from the Lorenz-63 system's simulations, the efficaciously learned structure recovers the dynamical equations. To the best of our knowledge, Informative Ensemble Kalman Learning is new. Results suggest that this approach to optimized Learning is promising.
