We develop interacting particle algorithms for learning latent variable models with energy-based priors. To do so, we leverage recent developments in particle-based methods for solving maximum marginal likelihood estimation (MMLE) problems. Specifically, we provide a continuous-time framework for learning latent energy-based models, by defining stochastic differential equations (SDEs) that provably solve the MMLE problem. We obtain a practical algorithm as a discretisation of these SDEs and provide theoretical guarantees for the convergence of the proposed algorithm. Finally, we demonstrate the empirical effectiveness of our method on synthetic and image datasets.

翻译：本文开发了用于学习具有能量先验的隐变量模型的交互粒子算法。为此，我们借鉴了基于粒子的方法在解决最大边际似然估计问题方面的最新进展。具体而言，我们通过定义可证明解决MMLE问题的随机微分方程，为学习隐能量模型提供了一个连续时间框架。我们将这些SDE离散化得到一个实用算法，并为所提算法的收敛性提供了理论保证。最后，我们在合成数据集和图像数据集上验证了该方法的实证有效性。