Several approximate inference methods have been proposed for deep discrete latent variable models. However, non-parametric methods which have previously been successfully employed for classical sparse coding models have largely been unexplored in the context of deep models. We propose a non-parametric iterative algorithm for learning discrete latent representations in such deep models. Additionally, to learn scale invariant discrete features, we propose local data scaling variables. Lastly, to encourage sparsity in our representations, we propose a Beta-Bernoulli process prior on the latent factors. We evaluate our spare coding model coupled with different likelihood models. We evaluate our method across datasets with varying characteristics and compare our results to current amortized approximate inference methods.

翻译：针对深度离散潜变量模型，学界已提出多种近似推断方法。然而，在经典稀疏编码模型中已被成功应用的非参数方法，在深度模型背景下仍鲜有探索。我们提出一种用于深度模型离散潜表示学习的非参数迭代算法。为学习尺度不变性离散特征，我们引入局部数据缩放变量；为增强表示的稀疏性，我们在潜因子中引入Beta-伯努利过程先验。将所提稀疏编码模型与不同似然模型相结合，我们评估了该方法在多种特征数据集上的表现，并与当前常用的摊销近似推断方法进行了对比分析。