Unsupervised learning of probabilistic models is a central yet challenging problem. Deep generative models have shown promising results in modeling complex distributions such as natural images (Radford et al.,2015), audio (Van Den Oord et al.,2016)and text (Bowman et al.,2015).

Neural Information Processing Systems http://nips.cc/

Motivatedbyrecent advances ingeometric representation learning, we propose a novel GNN architecture for learning representations on Riemannian manifolds with differentiable exponential and logarithmic maps.

We demonstrate the algorithm's effectiveness

A typical way to learn a latent variable model is maximizing the marginal likelihood [21].

This hasbeen shown to be insufficient for generating optimal representation for classification, and to find conditionally invariant representations, usually strong assumptions are needed.

Social media platforms capture diverse attack sequence samples through both machine and manual screening processes. Investigating effective ways to leverage these adversarial samples to enhance robustness is imperative.