Given the task definition and input, reply with output. In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Inferring the most likely configuration for a subset of variables of a joint distribution given the remaining ones which we refer to as co-generation is an important challenge that is computationally demanding for all but the simplest settings. This task has received a considerable amount of attention, particularly for classical ways of modeling distributions like structured prediction. In contrast, almost nothing is known about this task when considering recently proposed techniques for modeling high-dimensional distributions, particularly generative adversarial nets (GANs). Therefore, in this paper, we study the occurring challenges for co-generation with GANs. To address those challenges we develop an annealed importance sampling (AIS) based Hamiltonian Monte Carlo (HMC) co-generation algorithm. The presented approach significantly outperforms classical gradient-based methods on synthetic data and on CelebA.
Using annealed importance sampling on the co-generation problem.