We thank all reviewers for their critical comments and we address some questions below. Y es, we have tried the two-stage approach in our initial study. Similar as what many existing works reported [e.g. 1, MGDA is more robust and parameter-free. We did not perform statistical test since some baselines only used the default train/test split on MNIST. Our setup here also comply with existing works [e.g. 1, 5, 16, 34, 38, 39, 51, 52].

Density destructors are differentiable and invertible transforms that map multivariate PDFs of arbitrary structure (low entropy) into non-structured PDFs (maximum entropy). Multivariate Gaussianization and multivariate equalization are specific examples of this family, which break down the complexity of the original PDF through a set of elementary transforms that progressively remove the structure of the data. We demonstrate how this property of density destructive flows is connected to classical information theory, and how density destructors can be used to get more accurate estimates of information theoretic quantities. Experiments with total correlation and mutual information inmultivariate sets illustrate the ability of density destructors compared to competing methods. These results suggest that information theoretic measures may be an alternative optimization criteria when learning density destructive flows.

Generative models of graphs are well-known, but many existing models are limited in scalability and expressivity. We present a novel sequential graphical variational autoencoder operating directly on graphical representations of data. In our model, the encoding and decoding of a graph as is framed as a sequential deconstruction and construction process, respectively, enabling the the learning of a latent space. Experiments on a cycle dataset show promise, but highlight the need for a relaxation of the distribution over node permutations.

This post is part of Mashable's Masturbation Week. May is National Masturbation Month, so we're celebrating by exploring the many facets of self-love. Size matters -- at least when it comes to sex toys. If you're looking to treat yourself in the bedroom there are millions of sex toys to choose from, but one of the greatest things about masturbating is you're totally in control. You decide how to pleasure yourself, which means you have the option to go big -- like, really really big -- if you so choose.