标题： 用核方法传递期望传播消息 显示英文标题

标题： Passing Expectation Propagation Messages with Kernel Methods

摘要： 我们提出学习一个基于核的消息算子，该算子以因子节点的所有期望传播（EP）传入消息为输入，并生成传出消息。在普通EP中，计算传出消息涉及估计一个多变量积分，而该积分可能没有解析表达式。学习这样一个算子可以通过直接将所有传入消息映射到传出消息来绕过推理过程中昂贵的积分计算。该算子可以从训练数据（输入和输出消息的例子）中学习，从而可以对任何可采样的因子进行自动化推理。 显示英文摘要

摘要： We propose to learn a kernel-based message operator which takes as input all expectation propagation (EP) incoming messages to a factor node and produces an outgoing message. In ordinary EP, computing an outgoing message involves estimating a multivariate integral which may not have an analytic expression. Learning such an operator allows one to bypass the expensive computation of the integral during inference by directly mapping all incoming messages into an outgoing message. The operator can be learned from training data (examples of input and output messages) which allows automated inference to be made on any kind of factor that can be sampled.