We introduce the thermodynamic variational objective (TVO) for learning in both continuous and discrete deep generative models. The TVO arises from a key connection between variational inference and thermodynamic integration that results in a tighter lower bound to the log marginal likelihood than the standard variational variational evidence lower bound (ELBO) while remaining as broadly applicable. We provide a computationally efficient gradient estimator for the TVO that applies to continuous, discrete, and non-reparameterizable distributions and show that the objective functions used in variational inference, variational autoencoders, wake sleep, and inference compilation are all special cases of the TVO. We use the TVO to learn both discrete and continuous deep generative models and empirically demonstrate state of the art model and inference network learning.

翻译：我们引入热力变异目标(TVO),用于在连续和离散的深层基因模型中学习,TVO产生于变异推断与热力集成之间的关键联系,这种联系使得与日志的边际概率比标准变异证据的较低约束(ELBO)更紧密,同时保持广泛适用性。我们为TVO提供了一个计算高效的梯度估计器,适用于连续、离散和不可修复的分布,并表明在变异推断、变异自动电解器、觉醒和推断汇编中使用的客观功能都是TVO的特殊情况。我们利用TVO学习离散和连续的深层变异模型,并用经验展示艺术模型和推断网络学习的状况。