Providing accurate uncertainty estimations is essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaussian process models are generally regarded as the gold standard method for this task, but they can struggle with large, high-dimensional datasets. Combining deep neural networks with Gaussian process approximation techniques have shown promising results, but dimensionality reduction through standard deep neural network layers is not guaranteed to maintain the distance information necessary for Gaussian process models. We build on previous work by comparing the use of the singular value decomposition against a spectral-normalized dense layer as a feature extractor for a deep neural Gaussian process approximation model and apply it to a capacitance prediction problem for the High Voltage Converter Modulators in the Oak Ridge Spallation Neutron Source. Our model shows improved distance preservation and predicts in-distribution capacitance values with less than 1% error.

翻译：提供准确的不确定性估计对于构建可靠的机器学习模型至关重要，特别是在加速器系统等安全关键应用中。高斯过程模型通常被视为该任务的黄金标准方法，但它们在处理大规模高维数据集时存在局限性。将深度神经网络与高斯过程近似技术相结合已显示出良好前景，但通过标准深度神经网络层进行的降维无法保证保留高斯过程模型所需的距离信息。我们基于先前研究，比较了奇异值分解与谱归一化密集层作为特征提取器在深度神经高斯过程近似模型中的应用，并将其应用于橡树岭散裂中子源高压转换器调制器的电容预测问题。我们的模型展现出改进的距离保持能力，且对分布内电容值的预测误差小于1%。