84 citations · 304 across the 24 of their papers we have counts for
6 papers · 1 filter
A Systematic Survey on Deep Generative Models for Graph Generation
Xiaojie Guo, Liang Zhao
Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in…
Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement
Xiaojie Guo, Liang Zhao, Zhao Qin +3
Disentangled representation learning has recently attracted a significant amount of attention, particularly in the field of image representation learning. However, learning the dis…
Deep Multi-attributed Graph Translation with Node-Edge Co-evolution
Xiaojie Guo, Liang Zhao, Cameron Nowzari +3
Generalized from image and language translation, graph translation aims to generate a graph in the target domain by conditioning an input graph in the source domain. This promising…
Multi-stage Deep Classifier Cascades for Open World Recognition
Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu +4
At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase. However, real-world problem is far more…
Deep Graph Translation
Xiaojie Guo, Lingfei Wu, Liang Zhao
Inspired by the tremendous success of deep generative models on generating continuous data like image and audio, in the most recent year, few deep graph generative models have been…
Exclusivity Regularized Machine
Xiaojie Guo
It has been recognized that the diversity of base learners is of utmost importance to a good ensemble. This paper defines a novel measurement of diversity, termed as exclusivity. W…