5 citations · 10 across the 3 of their papers we have counts for
4 papers
Graph Condensation via Receptive Field Distribution Matching
Mengyang Liu, Shanchuan Li, Xinshi Chen +1
Graph neural networks (GNNs) enable the analysis of graphs using deep learning, with promising results in capturing structured information in graphs. This paper focuses on creating…
uGLAD: Sparse graph recovery by optimizing deep unrolled networks
Harsh Shrivastava, Urszula Chajewska, Robin Abraham +1
Probabilistic Graphical Models (PGMs) are generative models of complex systems. They rely on conditional independence assumptions between variables to learn sparse representations…
Efficient Dynamic Graph Representation Learning at Scale
Xinshi Chen, Yan Zhu, Haowen Xu +4
Dynamic graphs with ordered sequences of events between nodes are prevalent in real-world industrial applications such as e-commerce and social platforms. However, representation l…
Parametric FEM for Shape Optimization applied to Golgi Stack
Xinshi Chen, Eric Chung
The thesis is about an application of the shape optimization to the morphological evolution of Golgi stack. Golgi stack consists of multiple layers of cisternae. It is an organelle…