研究人员开发了GNet，这是一种新颖的高斯过程网络，旨在实现可扩展性和灵活性，它利用由高斯过程建模的非参数激活函数。为了解决计算和存储需求，他们引入了联合逆卡尔曼滤波器，这是一种通过避免协方差矩阵形成来加速训练和预测的算法。GNet在各种任务中表现出竞争力，包括非线性函数预测和真实世界数据回归，表明其在降低成本的大规模预测建模方面具有潜力。 AI
影响 引入了一种新颖的框架，以降低计算成本实现可扩展的预测建模。
排序理由 该集群包含一篇详细介绍新方法和模型的学术论文。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
arXiv:2607.10735v1 Announce Type: cross Abstract: We develop GNet, a scalable and flexible Gaussian process network with nonparametric activation functions modeled by Gaussian processes. To reduce computational and storage costs, we introduce the jointly inverse Kalman filter, a …
We develop GNet, a scalable and flexible Gaussian process network with nonparametric activation functions modeled by Gaussian processes. To reduce computational and storage costs, we introduce the jointly inverse Kalman filter, a fast algorithm together with closed-form expressio…
We develop GNet, a scalable and flexible Gaussian process network with nonparametric activation functions modeled by Gaussian processes. To reduce computational and storage costs, we introduce the jointly inverse Kalman filter, a fast algorithm together with closed-form expressio…