Graph convolutional networks (GCNs) allow us to learn topologically-aware node embeddings, which can be useful for classification or link prediction. However, they are unable to capture long-range dependencies between nodes without adding additional layers -- which in turn leads to over-smoothing and increased time and space complexity. Further, the complex dependencies between nodes make mini-batching challenging, limiting their applicability to large graphs. We propose a Scalable Multi-resolution Graph Representation Learning (SMGRL) framework that enables us to learn multi-resolution node embeddings efficiently. Our framework is model-agnostic and can be applied to any existing GCN model. We dramatically reduce training costs by training only on a reduced-dimension coarsening of the original graph, then exploit self-similarity to apply the resulting algorithm at multiple resolutions. The resulting multi-resolution embeddings can be aggregated to yield high-quality node embeddings that capture both long- and short-range dependencies. Our experiments show that this leads to improved classification accuracy, without incurring high computational costs.
Scalable Transit Delay Prediction at City Scale: A Systematic Approach with Multi-Resolution Feature Engineering and Deep Learning
Systems Engineering Department, École de technologie supérieure (ÉTS)（系统工程系，École de technologie supérieure（ÉTS））；Department of Computing and Information Systems, Trent University（计算与信息系统系，特伦特大学）
Scalable Generative Game Engine: Breaking the Resolution Wall via Hardware-Algorithm Co-Design
School of Microelectronics, Southern University of Science and Technology (SUSTech)（南方科技大学电子学院）；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；University of Waterloo（滑铁卢大学）；School of Marxism, Tsinghua University（清华大学马克思主义学院）；Zhongguancun Academy（中关村学院）
FiTv2: Scalable and Improved Flexible Vision Transformer for Diffusion Model
Shanghai AI Laboratory（上海人工智能实验室）；Chinese University of Hong Kong（香港中文大学）；Shanghai Jiao Tong University（上海交通大学）；University of Sydney（悉尼大学）；Tsinghua University（清华大学）
Virgo: Scalable Unsupervised Classification of Cosmological Shock Waves
Technische Universität München（慕尼黑工业大学）；Excellence Cluster ORIGINS（ORIGINS卓越集群）；Ludwig-Maximilians-Universität München（慕尼黑大学）；Flatiron Institute（熨斗研究所）；Max-Planck-Institut für Astrophysik（马克斯·普朗克天体物理研究所）
