论文标题
Bernoulli差异自动编码器的Elbo的下限
A lower bound for the ELBO of the Bernoulli Variational Autoencoder
论文作者
论文摘要
我们考虑用于二进制数据的变异自动编码器（VAE）。我们的主要创新是其训练目标的可解释下限，这种VAE的修改初始化和结构，可实现更快的培训，以及通过使用PCA找到潜在空间的适当维度的决策支持。数值示例说明了我们的理论结果和新体系结构的性能。
We consider a variational autoencoder (VAE) for binary data. Our main innovations are an interpretable lower bound for its training objective, a modified initialization and architecture of such a VAE that leads to faster training, and a decision support for finding the appropriate dimension of the latent space via using a PCA. Numerical examples illustrate our theoretical result and the performance of the new architecture.
