46 citations · 63 across the 15 of their papers we have counts for
16 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…
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…
Seven open problems in applied combinatorics
Sinan G. Aksoy, Ryan Bennink, Yuzhou Chen +10
We present and discuss seven different open problems in applied combinatorics. The application areas relevant to this compilation include quantum computing, algorithmic differentia…
Efficient Planning of Multi-Robot Collective Transport using Graph Reinforcement Learning with Higher Order Topological Abstraction
Steve Paul, Wenyuan Li, Brian Smyth +3
Efficient multi-robot task allocation (MRTA) is fundamental to various time-sensitive applications such as disaster response, warehouse operations, and construction. This paper tac…
Learning on Health Fairness and Environmental Justice via Interactive Visualization
Abdullah-Al-Raihan Nayeem, Ignacio Segovia-Dominguez, Huikyo Lee +5
This paper introduces an interactive visualization interface with a machine learning consensus analysis that enables the researchers to explore the impact of atmospheric and socioe…
Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets
Yuzhou Chen, Tian Jiang, Miguel Heleno +2
Nowadays, it is broadly recognized in the power system community that to meet the ever expanding energy sector's needs, it is no longer possible to rely solely on physics-based mod…
ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery
Andac Demir, Baris Coskunuzer, Ignacio Segovia-Dominguez +3
In computer-aided drug discovery (CADD), virtual screening (VS) is used for identifying the drug candidates that are most likely to bind to a molecular target in a large library of…
BScNets: Block Simplicial Complex Neural Networks
Yuzhou Chen, Yulia R. Gel, H. Vincent Poor
Simplicial neural networks (SNN) have recently emerged as the newest direction in graph learning which expands the idea of convolutional architectures from node space to simplicial…
Topological Relational Learning on Graphs
Yuzhou Chen, Baris Coskunuzer, Yulia R. Gel
Graph neural networks (GNNs) have emerged as a powerful tool for graph classification and representation learning. However, GNNs tend to suffer from over-smoothing problems and are…
Using NASA Satellite Data Sources and Geometric Deep Learning to Uncover Hidden Patterns in COVID-19 Clinical Severity
Ignacio Segovia-Dominguez, Huikyo Lee, Zhiwei Zhen +5
As multiple adverse events in 2021 illustrated, virtually all aspects of our societal functioning -- from water and food security to energy supply to healthcare -- more than ever d…
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting
Yuzhou Chen, Ignacio Segovia-Dominguez, Yulia R. Gel
There recently has been a surge of interest in developing a new class of deep learning (DL) architectures that integrate an explicit time dimension as a fundamental building block…
LFGCN: Levitating over Graphs with Levy Flights
Yuzhou Chen, Yulia R. Gel, Konstantin Avrachenkov
Due to high utility in many applications, from social networks to blockchain to power grids, deep learning on non-Euclidean objects such as graphs and manifolds, coined Geometric D…
Snowboot: Bootstrap Methods for Network Inference
Yuzhou Chen, Yulia R. Gel, Vyacheslav Lyubchich +1
Complex networks are used to describe a broad range of disparate social systems and natural phenomena, from power grids to customer segmentation to human brain connectome. Challeng…