You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: In concurrently published work, Locatello et al. [34] question the plausibility of learning unsupervised disentangled representations with meaningful features, based on theoretical analyses showing an equivalence class of generative models, many members of which could be entangled.Though their analysis is sound, we posit a counterargument to their conclusions, based on the stochastic nature of the encodings used during training.Namely, that this stochasticity means that they need not give rise to the same elbo scores (an important exception is the rotational invariance for isotropic Gaussian priors).Essentially, the encoding noise forces nearby encodings to relate to similar datapoints, while standard choices for the likelihood distribution (e.g. assuming conditional independence) ensure that information is stored in the encodings, not just in the generative network.These restrictions mean that the elbo prefers smooth representations and, provided the prior is not rotationally invariant, means that there no longer need be a class of different representations with the same elbo; simpler representations are preferred to more complex ones.

Question: What is the role of encoding noise and the likelihood distribution in VAE representations?
In VAE representation, the encoding noise forces nearby encodings to relate to similar datapoints, while standard choices for the likelihood distribution ensure that information is stored in the encodings, not just in the generative network