46 citations · 47 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
Smart Vectorizations for Single and Multiparameter Persistence
Baris Coskunuzer, CUneyt Gurcan Akcora, Ignacio Segovia Dominguez +3
The machinery of topological data analysis becomes increasingly popular in a broad range of machine learning tasks, ranging from anomaly detection and manifold learning to graph cl…