3 citations · 3 across the 2 of their papers we have counts for
3 papers
Pseudo-Riemannian Embedding Models for Multi-Relational Graph Representations
Saee Paliwal, Angus Brayne, Benedek Fabian +2
In this paper we generalize single-relation pseudo-Riemannian graph embedding models to multi-relational networks, and show that the typical approach of encoding relations as manif…
Directed Graph Embeddings in Pseudo-Riemannian Manifolds
Aaron Sim, Maciej Wiatrak, Angus Brayne +2
The inductive biases of graph representation learning algorithms are often encoded in the background geometry of their embedding space. In this paper, we show that general directed…
Stabilizing Generative Adversarial Networks: A Survey
Maciej Wiatrak, Stefano V. Albrecht, Andrew Nystrom
Generative Adversarial Networks (GANs) are a type of generative model which have received much attention due to their ability to model complex real-world data. Despite their recent…