On the Influence of Enforcing Model Identifiability on Learning dynamics of Gaussian Mixture Models. (arXiv:2206.08598v1 [cs.LG])

A common way to learn and analyze statistical models is to consider
operations in the model parameter space. But what happens if we optimize in the
parameter space and there is no one-to-one mapping between the parameter space
and the underlying statistical model space? Such cases frequently occur for
hierarchical models which include statistical mixtures or stochastic neural
networks, and these models are said to be singular. Singular models reveal
several important and well-studied problems in machine learning like the …

arxiv dynamics influence learning lg