A5035058164- Topological and Geometric Data Analysis
- Digital Image Processing Techniques
- Data Management and Algorithms
- Human Mobility and Location-Based Analysis
- Transportation Planning and Optimization
- Neural Networks and Applications
- Advanced Graph Neural Networks
- Computational Physics and Python Applications
- Traffic Prediction and Management Techniques
RWTH Aachen University
2023-2024
Passengers of public transportation nowadays expect reliable and accurate travel information. The need for occupancy information is becoming more prevalent in intelligent transport systems as people started avoiding overcrowded vehicles during the COVID-19 pandemic. Furthermore, companies require forecasts to improve service quality. We present a novel approach prediction passenger numbers that enhances day-ahead with real-time data. first train baseline predictor on historical automatic...
We introduce topox, a Python software suite that provides reliable and user-friendly building blocks for computing machine learning on topological domains extend graphs: hypergraphs, simplicial, cellular, path combinatorial complexes. topox consists of three packages: toponetx facilitates constructing these domains, including working with nodes, edges higher-order cells; topoembedx methods to embed into vector spaces, akin popular graph-based embedding algorithms such as node2vec; topomodelx...
We consider the problem of classifying trajectories on a discrete or discretised 2-dimensional manifold modelled by simplicial complex. Previous works have proposed to project into harmonic eigenspace Hodge Laplacian, and then cluster resulting embeddings. However, if considered space has vanishing homology (i.e., no "holes"), 1-Hodge Laplacian is trivial thus approach fails. Here we propose view this issue akin sensor placement present an algorithm that aims learn "optimal holes"...
This paper presents the computational challenge on topological deep learning that was hosted within ICML 2023 Workshop Topology and Geometry in Machine Learning. The competition asked participants to provide open-source implementations of neural networks from literature by contributing python packages TopoNetX (data processing) TopoModelX (deep learning). attracted twenty-eight qualifying submissions its two-month duration. describes design summarizes main findings.