We introduce Markov Neural Processes (MNPs), a new class of Stochastic Processes (SPs) which are constructed by stacking sequences of neural parameterised Markov transition operators in function space. We prove that these Markov transition operators can preserve the exchangeability and consistency of SPs. Therefore, the proposed iterative construction adds substantial flexibility and expressivity to the original framework of Neural Processes (NPs) without compromising consistency or adding restrictions. Our experiments demonstrate clear advantages of MNPs over baseline models on a variety of tasks.

翻译：我们提出马尔可夫神经过程（MNPs），这是一类新的随机过程（SPs），通过在函数空间中堆叠神经参数化的马尔可夫转移算子序列来构建。我们证明这些马尔可夫转移算子能够保持随机过程的交换性和一致性。因此，所提出的迭代构造方法在不牺牲一致性或增加限制的情况下，为原始神经过程（NPs）框架增添了显著的灵活性和表达力。我们的实验表明，MNPs在多种任务上均优于基线模型。