Conditional Neural Processes (CNPs) are a class of metalearning models popular for combining the runtime efficiency of amortized inference with reliable uncertainty quantification. Many relevant machine learning tasks, such as in spatio-temporal modeling, Bayesian Optimization and continuous control, inherently contain equivariances -- for example to translation -- which the model can exploit for maximal performance. However, prior attempts to include equivariances in CNPs do not scale effectively beyond two input dimensions. In this work, we propose Relational Conditional Neural Processes (RCNPs), an effective approach to incorporate equivariances into any neural process model. Our proposed method extends the applicability and impact of equivariant neural processes to higher dimensions. We empirically demonstrate the competitive performance of RCNPs on a large array of tasks naturally containing equivariances.

翻译：条件神经过程（CNPs）是一类元学习模型，因将摊销推理的运行时效率与可靠的不确定性量化相结合而广受欢迎。许多相关的机器学习任务（例如时空建模、贝叶斯优化和连续控制）天然包含等变性（例如平移等变性），模型可利用这些性质实现最优性能。然而，先前将等变性纳入CNPs的尝试在超过两个输入维度时难以有效扩展。本文提出关系条件神经过程（RCNPs），一种将等变性有效融入任意神经过程模型的方法。所提出的方法将等变神经过程的适用范围与影响扩展至更高维度。我们通过大量天然包含等变性的任务，实验证明了RCNPs的竞争性性能。