Interpretable probabilistic embeddings: bridging the gap between topic models and neural networks

Anna Potapenko, Artem Popov, Konstantin Vorontsov

We consider probabilistic topic models and more recent word embedding techniques from a perspective of learning hidden semantic representations. Inspired by a striking similarity o…