论文标题
NP-prov：仅位置与位置差异的神经过程
NP-PROV: Neural Processes with Position-Relevant-Only Variances
论文作者
论文摘要
神经过程（NPS）家族在给定上下文数据中对功能上的分布进行编码，并在未知位置解码后均值和差异。由于平均值和方差来自相同的潜在空间，因此它们可能会在功能值波动放大模型不确定性的室外任务上失败。我们提出了一个新成员，名为“神经过程”，具有仅位置与位置相关的差异（NP-Prov）。 NP-prov假设接近上下文点的目标点具有较小的不确定性，而不管该位置的功能值如何。所得的方法从功能值相关的空间和位置相关的潜在空间分别得出了平均值和差异。我们对合成和现实世界数据集的评估表明，NP-Prov可以实现最新的可能性，同时在功能值中存在漂移时保留有界方差。
Neural Processes (NPs) families encode distributions over functions to a latent representation, given context data, and decode posterior mean and variance at unknown locations. Since mean and variance are derived from the same latent space, they may fail on out-of-domain tasks where fluctuations in function values amplify the model uncertainty. We present a new member named Neural Processes with Position-Relevant-Only Variances (NP-PROV). NP-PROV hypothesizes that a target point close to a context point has small uncertainty, regardless of the function value at that position. The resulting approach derives mean and variance from a function-value-related space and a position-related-only latent space separately. Our evaluation on synthetic and real-world datasets reveals that NP-PROV can achieve state-of-the-art likelihood while retaining a bounded variance when drifts exist in the function value.
