5 papers
Challenges in 3D Data Synthesis for Training Neural Networks on Topological Features
Dylan Peek, Matthew P. Skerritt, Siddharth Pritam +1
Topological Data Analysis (TDA) involves techniques of analyzing the underlying structure and connectivity of data. However, traditional methods like persistent homology can be com…
Noise-Robust Topology Estimation of 2D Image Data via Neural Networks and Persistent Homology
Dylan Peek, Matthew P. Skerritt, Stephan Chalup
Persistent Homology (PH) and Artificial Neural Networks (ANNs) offer contrasting approaches to inferring topological structure from data. In this study, we examine the noise robust…
Time Series Analysis of Spiking Neural Systems via Transfer Entropy and Directed Persistent Homology
Dylan Peek, Siddharth Pritam, Matthew P. Skerritt +1
We present a topological framework for analysing neural time series that integrates Transfer Entropy (TE) with directed Persistent Homology (PH) to characterize information flow in…
Generating Topologically and Geometrically Diverse Manifold Data in Dimensions Four and Below
Khalil Mathieu Hannouch, Stephan Chalup
Understanding the topological characteristics of data is important to many areas of research. Recent work has demonstrated that synthetic 4D image-type data can be useful to train…
Synthetic Data Generation and Deep Learning for the Topological Analysis of 3D Data
Dylan Peek, Matt P. Skerritt, Stephan Chalup
This research uses deep learning to estimate the topology of manifolds represented by sparse, unordered point cloud scenes in 3D. A new labelled dataset was synthesised to train ne…