2 citations · 4 across the 7 of their papers we have counts for
7 papers
TopoGCL: Topological Graph Contrastive Learning
Yuzhou Chen, Jose Frias, Yulia R. Gel
Graph contrastive learning (GCL) has recently emerged as a new concept which allows for capitalizing on the strengths of graph neural networks (GNNs) to learn rich representations…
Tensor-view Topological Graph Neural Network
Tao Wen, Elynn Chen, Yuzhou Chen
Graph classification is an important learning task for graph-structured data. Graph neural networks (GNNs) have recently gained growing attention in graph learning and have shown s…
EMP: Effective Multidimensional Persistence for Graph Representation Learning
Ignacio Segovia-Dominguez, Yuzhou Chen, Cuneyt G. Akcora +4
Topological data analysis (TDA) is gaining prominence across a wide spectrum of machine learning tasks that spans from manifold learning to graph classification. A pivotal techniqu…
Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence
Baris Coskunuzer, Ignacio Segovia-Dominguez, Yuzhou Chen +1
Learning time-evolving objects such as multivariate time series and dynamic networks requires the development of novel knowledge representation mechanisms and neural network archit…
SpOctA: A 3D Sparse Convolution Accelerator with Octree-Encoding-Based Map Search and Inherent Sparsity-Aware Processing
Dongxu Lyu, Zhenyu Li, Yuzhou Chen +3
Point-cloud-based 3D perception has attracted great attention in various applications including robotics, autonomous driving and AR/VR. In particular, the 3D sparse convolution (Sp…
Topological Pooling on Graphs
Yuzhou Chen, Yulia R. Gel
Graph neural networks (GNNs) have demonstrated a significant success in various graph learning tasks, from graph classification to anomaly detection. There recently has emerged a n…