By composing graphical models with deep learning architectures, we learn generative models with the strengths of both frameworks. The structured variational autoencoder (SVAE) inherits structure and interpretability from graphical models, and flexible likelihoods for high-dimensional data from deep learning, but poses substantial optimization challenges. We propose novel algorithms for learning SVAEs, and are the first to demonstrate the SVAE's ability to handle multimodal uncertainty when data is missing by incorporating discrete latent variables. Our memory-efficient implicit differentiation scheme makes the SVAE tractable to learn via gradient descent, while demonstrating robustness to incomplete optimization. To more rapidly learn accurate graphical model parameters, we derive a method for computing natural gradients without manual derivations, which avoids biases found in prior work. These optimization innovations enable the first comparisons of the SVAE to state-of-the-art time series models, where the SVAE performs competitively while learning interpretable and structured discrete data representations.

翻译：通过将图模型与深度学习架构相结合，我们学习兼具两者优势的生成模型。结构化变分自编码器（SVAE）继承了图模型的结构的可解释性以及深度学习对高维数据的灵活似然函数，但其优化面临重大挑战。我们提出了学习SVAE的新算法，并首次展示了SVAE通过融入离散潜变量处理数据缺失时多模态不确定性的能力。所提出的内存高效的隐式微分方案使得SVAE能够通过梯度下降进行可训练学习，同时展现出对不完整优化的鲁棒性。为更快地学习精确的图模型参数，我们推导出一种无需手动推导即可计算自然梯度的方法，从而避免了先前工作中存在的偏差。这些优化创新使SVAE能够首次与最先进的时间序列模型进行对比，其在学习可解释且结构化的离散数据表示方面展现出竞争性表现。