The bottleneck introduced by the low-dimensional latent space is what characterizes the compression and representation learning capabilities ofautoencoders.

Marginal likelihood (ML), sometimes calledevidence, is a central quantity in Bayesian learning as it measures how well a model can describe a particular dataset.

Marginal likelihood (ML), sometimes calledevidence, is a central quantity in Bayesian learning as it measures how well a model can describe a particular dataset.

But it can also be problematic since it can induce inconsistencies in, e.g., Hessian-based flatness measures,

We focus on a stochastic learning model where the learner observes a finite set of training examples and the output of the learning process is a data-dependent distribution over a space of hypotheses.