论文标题
Siren-Vae：利用流量并摊销贝叶斯网络
SIReN-VAE: Leveraging Flows and Amortized Inference for Bayesian Networks
论文作者
论文摘要
关于变异自动编码器的初步工作假设具有简单分布的独立潜在变量。随后的工作探索了整合更复杂的分布和依赖性结构：包括编码器网络中的流动归一化允许潜在变量非线性地纠缠，为近似后部创建了更丰富的分布类别，并堆叠了潜在变量的堆叠层，可以为产生的模型指定更复杂的较高的较高的较高的较高的较高的较高的较高的priors。这项工作探讨了将贝叶斯网络指定的任意依赖性结构纳入VAE的任意依赖性结构。这是通过使用图形残差流的先验和推理网络来实现的 - 残余流 - 通过掩盖流量残留块的重量矩阵来编码有条件独立性。我们比较了模型在几个合成数据集上的性能，并在数据范围设置中显示了其潜力。
Initial work on variational autoencoders assumed independent latent variables with simple distributions. Subsequent work has explored incorporating more complex distributions and dependency structures: including normalizing flows in the encoder network allows latent variables to entangle non-linearly, creating a richer class of distributions for the approximate posterior, and stacking layers of latent variables allows more complex priors to be specified for the generative model. This work explores incorporating arbitrary dependency structures, as specified by Bayesian networks, into VAEs. This is achieved by extending both the prior and inference network with graphical residual flows - residual flows that encode conditional independence by masking the weight matrices of the flow's residual blocks. We compare our model's performance on several synthetic datasets and show its potential in data-sparse settings.
