We propose a general and scalable approximate sampling strategy for
probabilistic models with discrete variables. Our approach uses gradients of
the likelihood function with respect to its discrete inputs to propose updates
in a Metropolis-Hastings sampler. We show empirically that this approach
outperforms generic samplers in a number of difficult settings including Ising
models, Potts models, restricted Boltzmann machines, and factorial hidden
Markov models. We also demonstrate the use of our improved sampler for training
deep energy-based models on high dimensional discrete data. This approach
outperforms variational auto-encoders and existing energy-based models.
Finally, we give bounds showing that our approach is near-optimal in the class
of samplers which propose local updates.

使用似然函数关于离散变量的梯度来提议更新，在一些复杂的模型上，我们的采样方法包括 Ising 模型、Potts 模型、受限玻尔兹曼机和分步隐马尔可夫模型等方面表现出优异的性能，并且有效提升以往在高维离散数据处理方面经常使用的变分自编码器和现有基于能量模型的方法的性能。同时我们证明，该方法是在局部更新的抽样器中几乎是最优的。

离散分布的可伸缩抽样：梯度抽取错误

Oops I Took A Gradient: Scalable Sampling for Discrete Distributions

Deep energy-based models (EBMs) are very flexible in distribution
parametrization but computationally challenging because of the intractable
partition function. They are typically trained via maximum likelihood, using
contrastive divergence to approximate the gradient of the KL divergence between
data and model distribution. While KL divergence has many desirable properties,
other f-divergences have shown advantages in training implicit density
generative models such as generative adversarial networks. In this paper, we
propose a general variational framework termed f-EBM to train EBMs using any
desired f-divergence. We introduce a corresponding optimization algorithm and
prove its local convergence property with non-linear dynamical systems theory.
Experimental results demonstrate the superiority of f-EBM over contrastive
divergence, as well as the benefits of training EBMs using f-divergences other
than KL.

本文提出了 f-EBM 框架，该框架利用 f 散度来训练 EBM，实验结果表明 f-EBM 的优越性以及使用 f 散度训练 EBM 的好处。