Title: Adversarial Variational Inference and Learning in Markov Random Fields,
Abstract: Markov random fields (MRFs) find applications in a variety of machine
learning areas, while the inference and learning of such models are challenging
in general. In this paper, we propose the Adversarial Variational Inference and
Learning (AVIL) algorithm to solve the problems with a minimal assumption about
the model structure of an MRF. AVIL employs two variational distributions to
approximately infer the latent variables and estimate the partition function,
respectively. The variational distributions, which are parameterized as neural
networks, provide an estimate of the negative log likelihood of the MRF. On one
hand, the estimate is in an intuitive form of approximate contrastive free
energy. On the other hand, the estimate is a minimax optimization problem,
which is solved by stochastic gradient descent in an alternating manner. We
apply AVIL to various undirected generative models in a fully black-box manner
and obtain better results than existing competitors on several real datasets.