论文标题
线性潜在变量模型的后塌陷
Posterior Collapse of a Linear Latent Variable Model
论文作者
论文摘要
这项工作确定了贝叶斯深度学习实践中经常发生的一种后倒塌的存在和原因。对于包含线性变分自动编码器作为特殊情况的一般线性潜在变量模型，我们精确地识别后塌陷的性质是由于先验而导致的均值和平均值的正则化之间的竞争。我们的结果表明，后置崩溃可能与神经崩溃和维度崩溃有关，并且可能是对更深层次结构学习的一般学习问题的子类。
This work identifies the existence and cause of a type of posterior collapse that frequently occurs in the Bayesian deep learning practice. For a general linear latent variable model that includes linear variational autoencoders as a special case, we precisely identify the nature of posterior collapse to be the competition between the likelihood and the regularization of the mean due to the prior. Our result suggests that posterior collapse may be related to neural collapse and dimensional collapse and could be a subclass of a general problem of learning for deeper architectures.
