首页>
外国专利>
QUANTIFYING THE PREDICTIVE UNCERTAINTY OF NEURAL NETWORKS VIA RESIDUAL ESTIMATION WITH I/O KERNEL
QUANTIFYING THE PREDICTIVE UNCERTAINTY OF NEURAL NETWORKS VIA RESIDUAL ESTIMATION WITH I/O KERNEL
展开▼
机译:基于I/O核残差估计的神经网络预测不确定性量化
展开▼
页面导航
摘要
著录项
相似文献
摘要
A residual estimation with an I/O kernel ("RIO") framework provides estimates of predictive uncertainty of neural networks, and reduces their point-prediction errors. The process captures neural network ("NN") behavior by estimating their residuals with an I/O kernel using a modified Gaussian process ("GP"). RIO is applicable to real-world problems, and, by using a sparse GP approximation, scales well to large datasets. RIO can be applied directly to any pretrained NNs without modifications to model architecture or training pipeline.
展开▼