Graph Topological Features via GAN

no code implementations • ICLR 2018 • Weiyi Liu, Hal Cooper, Min-hwan Oh

Inspired by the success of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological features from a single arbitrary input graph via GANs.

Reparameterizing the Birkhoff Polytope for Variational Permutation Inference

no code implementations • 26 Oct 2017 • Scott W. Linderman, Gonzalo E. Mena, Hal Cooper, Liam Paninski, John P. Cunningham

Many matching, tracking, sorting, and ranking problems require probabilistic reasoning about possible permutations, a set that grows factorially with dimension.

Bayesian Inference Combinatorial Optimization +1

Learning Graph Topological Features via GAN

no code implementations • 11 Sep 2017 • Weiyi Liu, Hal Cooper, Min Hwan Oh, Sailung Yeung, Pin-Yu Chen, Toyotaro Suzumura, Lingli Chen

Inspired by the generation power of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological features from a single arbitrary input graph via GANs.

Can GAN Learn Topological Features of a Graph?

no code implementations • 19 Jul 2017 • Weiyi Liu, Pin-Yu Chen, Hal Cooper, Min Hwan Oh, Sailung Yeung, Toyotaro Suzumura

This paper is first-line research expanding GANs into graph topology analysis.

Graph Reconstruction