研究人员提出了太分布函数隐地因因子回归(GPLFR)，一个新型号，该号目的是从有限的训练数据中预测高维度的输出。该方法通过将压缩和预测结合到单一目标中，解决了现有多输出太分布函数回归和压缩后预测管道的限制。GPLFR已成功应用于创建地球石外行星全球气候模型的首个空间分辨率的模拟器。 AI
影响 介绍了一种新型方法，用于处理有限数据的复杂预测任务，有可能推动科学模拟的进展。
排序理由 这是一篇描述新模型的研究论文。
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arXiv:2606.06576v1 Announce Type: cross Abstract: In the sciences, regression tasks often require predicting high-dimensional outputs from few training examples. Multi-output Gaussian processes excel in low-data regimes but typically struggle with high-dimensional outputs. Compre…
In the sciences, regression tasks often require predicting high-dimensional outputs from few training examples. Multi-output Gaussian processes excel in low-data regimes but typically struggle with high-dimensional outputs. Compress-then-predict pipelines such as PCA-GP (principa…