论文标题
非平稳异方差高斯流程的不确定性分解用于主动学习
Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning
论文作者
论文摘要
高斯工艺是许多领域使用的贝叶斯非参数模型。在这项工作中，我们提出了一个非平稳的异质性高斯过程模型，可以通过基于梯度的技术来学习。我们通过将总体不确定性分离为息肉（不可删除）和认知（模型）不确定性来证明所提出的模型的解释性。我们说明了对主动学习问题的衍生认识不确定性的可用性。我们通过多个数据集的各种消融来证明我们的模型的功效。
Gaussian processes are Bayesian non-parametric models used in many areas. In this work, we propose a Non-stationary Heteroscedastic Gaussian process model which can be learned with gradient-based techniques. We demonstrate the interpretability of the proposed model by separating the overall uncertainty into aleatoric (irreducible) and epistemic (model) uncertainty. We illustrate the usability of derived epistemic uncertainty on active learning problems. We demonstrate the efficacy of our model with various ablations on multiple datasets.
