Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification - Research Database, The University of York

By the same authors

Learning Binary Code for Fast Nearest Subspace Search
Zhou, L., Xiao, B., Liu, X., Zhou, J. & Hancock, E. R., 12 Sep 2019
Article in Pattern Recognition
Seeking Affinity Structure: Strategies for Improving m-best Graph Matching
Curado, M., Escolano, F., Lozano, M. A. & Hancock, E. R., 10 Sep 2019
Article in Information Sciences
Discriminative Features Matter: Multi-layer Bilinear Pooling for Camera Localization
Wang, X., Wang, X., Wang, C., Bai, X., Wu, J. & Hancock, E. R., 23 Jul 2019, (Accepted/In press). 12 p.
Research output: Contribution to conference › Paper
Deep Supervised Hashing using Symmetric Relative Entropy
Zhang, X., Zhou, L., Bai, X., Luan, X., Luo, J. & Hancock, E. R., 11 Jul 2019
Article in Pattern Recognition Letters
Dirichlet Densifier Bounds: Densifying Beyond the Spectral Gap Constraint
Curado, M., Lozano, M. A., Escolano, F. & Hancock, E. R., 1 Jul 2019
Article in Pattern Recognition Letters

Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Links

http://arxiv.org/abs/1902.09936

Author(s)

Lu Bai
Lixin Cui
Yuhang Jiao
Edwin R. Hancock

Department/unit(s)

Computer Science

Publication details

Title of host publication	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Würzburg, Germany, 16th - 20th of September 2019
Date	Published - 2019
Original language	English

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations

York staff: edit these data