发表机构
University of California, Santa Barbara（加利福尼亚大学圣塔芭芭拉分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究开发GNet这种可扩展灵活高斯过程网络，引入联合逆卡尔曼滤波器降低成本，通过统一优化设置，在多种测试问题中展现竞争力，能广泛应用于大规模预测建模并降低计算和存储成本。
AI 中文摘要
我们开发了GNet，一种具有由高斯过程建模的非参数激活函数的可扩展灵活高斯过程网络。为降低计算和存储成本，引入联合逆卡尔曼滤波器，一种快速算法及梯度闭式表达式，无需形成协方差矩阵即可加速模型训练和预测。在统一优化设置下，GNet在多种测试问题中表现出竞争力，包括预测非线性函数、真实世界数据的非参数回归等。其强大性能表明可广泛应用于大规模预测建模，显著降低计算和存储成本。
英文摘要
We build GNet, a scalable and flexible Gaussian process network with nonparametric activation functions. The key computational contribution is to make estimation of GP activations computationally scalable without forming covariance matrices explicitly. We provide theoretical studies comparing function classes with a fixed architecture, and derive predictive risk bounds for GNets. Using essentially the same architecture and optimization settings, GNet shows competitive performance across a diverse range of test problems, including predicting nonlinear functions, nonparametric regression of real-world data, and predicting one-body direct correlation functions with high-dimensional inputs in classical density functional theory. The strong performance of GNet suggests that flexible nonparametric neurons can substantially reduce the number of trainable parameters while remaining computationally scalable.
