Numerous recent works utilize bi-Lipschitz regularization of neural network layers to preserve relative distances between data instances in the feature spaces of each layer. This distance sensitivity with respect to the data aids in tasks such as uncertainty calibration and out-of-distribution (OOD) detection. In previous works, features extracted with a distance sensitive model are used to construct feature covariance matrices which are used in deterministic uncertainty estimation or OOD detection. However, in cases where there is a distribution over tasks, these methods result in covariances which are sub-optimal, as they may not leverage all of the meta information which can be shared among tasks. With the use of an attentive set encoder, we propose to meta learn either diagonal or diagonal plus low-rank factors to efficiently construct task specific covariance matrices. Additionally, we propose an inference procedure which utilizes scaled energy to achieve a final predictive distribution which can better separate OOD data, and is well calibrated under a distributional dataset shift.

翻译：最近许多作品都利用神经网络层的双利普施茨正规化来保持每一层地貌空间中的数据实例之间的相对距离。对于诸如不确定性校准和分配外检测(OOD)等任务中的数据辅助设备的这种距离敏感度。在以往的作品中,利用远程敏感模型提取的特征被用于构建用于确定性不确定性估计或OOD检测的特征共变矩阵。然而,在任务分布分布上的情况下,这些方法导致相异性是次最佳的,因为它们可能无法利用所有可共享的任务的元信息。我们建议利用一套注意的编码编码器进行元化学习,要么是对角或对角加低位系数,以高效地构建任务特定的共变矩阵。此外,我们建议采用一种推论程序,利用规模的能量实现最终的预测分布分布分布式分布式分布式数据,并在分布式数据集变化下进行精确校准。