Learning general latent-variable probabilistic graphical models is a key
theoretical challenge in machine learning and artificial intelligence. All
previous methods, including the EM algorithm and the spectral algorithms, face
severe limitations that largely restrict their applicability and affect their
performance. In order to overcome these limitations, in this paper we introduce
a novel formulation of message-passing inference over junction trees named
predictive belief propagation, and propose a new learning and inference
algorithm for general latent-variable graphical models based on this
formulation. Our proposed algorithm reduces the hard parameter learning problem
into a sequence of supervised learning problems, and unifies the learning of
different kinds of latent graphical models into a single learning framework,
which is local-optima-free and statistically consistent. We then give a proof
of the correctness of our algorithm and show in experiments on both synthetic
and real datasets that our algorithm significantly outperforms both the EM
algorithm and the spectral algorithm while also being orders of magnitude
faster to compute.

提出了一种基于预测信念传播的新型学习和推断算法，对于一般潜变量图模型，算法将硬参数学习问题简化为一系列监督学习问题，并将学习不同种类的潜变量图模型统一到一个局部最优、统计一致的学习框架中，此算法在合成和真实数据集上显著优于前人方法，也更快地计算。

利用预测信念传播学习通用潜变量图模型

Learning General Latent-Variable Graphical Models with Predictive Belief  Propagation

We propose to study the problem of few-shot learning with the prism of
inference on a partially observed graphical model, constructed from a
collection of input images whose label can be either observed or not. By
assimilating generic message-passing inference algorithms with their
neural-network counterparts, we define a graph neural network architecture that
generalizes several of the recently proposed few-shot learning models. Besides
providing improved numerical performance, our framework is easily extended to
variants of few-shot learning, such as semi-supervised or active learning,
demonstrating the ability of graph-based models to operate well on 'relational'
tasks.

本文提出通过部分观察的图模型推断来研究少样本学习问题，结合传递消息推断算法与神经网络的图神经网络架构，可以泛化多个最近提出的少样本模型，并可以扩展到半监督学习和主动学习等少样本学习变种，展示了图形模型在关系型任务上操作的能力。