论文标题
贝叶斯深度学习在分析异常扩散中的误差估计
Bayesian deep learning for error estimation in the analysis of anomalous diffusion
论文作者
论文摘要
现代的单粒子跟踪技术在各种系统中产生了广泛的扩散运动，从活细胞的单分子运动到运动生态学。任务是破译数据中编码的物理机制，从而更好地理解探测系统。我们在这里增加了最近提出的用于解码异常扩散数据的机器学习技术，除了预测的输出外，还包括一个不确定性估计值。为了避免黑盒问题，贝叶斯深度学习技术称为随机赋予 - 平均高斯 - 用于训练模型的分类和单个partipection-traijection的异常扩散指数的回归。在评估其性能时，我们发现这些模型可以实现良好的误差估计，同时保持高预测精度。在对输出不确定性预测的分析中，我们将这些预测与基本扩散模型的属性联系起来，从而提供了有关机器学习过程和输出相关性的见解。
Modern single-particle-tracking techniques produce extensive time-series of diffusive motion in a wide variety of systems, from single-molecule motion in living-cells to movement ecology. The quest is to decipher the physical mechanisms encoded in the data and thus to better understand the probed systems. We here augment recently proposed machine-learning techniques for decoding anomalous-diffusion data to include an uncertainty estimate in addition to the predicted output. To avoid the Black-Box-Problem a Bayesian-Deep-Learning technique named Stochastic-Weight-Averaging-Gaussian is used to train models for both the classification of the diffusion model and the regression of the anomalous diffusion exponent of single-particle-trajectories. Evaluating their performance, we find that these models can achieve a well-calibrated error estimate while maintaining high prediction accuracies. In the analysis of the output uncertainty predictions we relate these to properties of the underlying diffusion models, thus providing insights into the learning process of the machine and the relevance of the output.
