Charalambos Poullis

Topic

graph neural networks

A collection of 2 issues

HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning

The paper "HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning" by Naghmeh Shafiee Roudbari, Ursula Eicker, Charalambos Poullis, Zachary Patterson has been accepted for publication in 19th International Symposium on Visual Computing (ISVC), 2024. TL:DR: A hybrid graph learning model using LiDAR terrain elevation

Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction

The preprint of our paper Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction is available on arxiv.org. The work is co-authored by Naghmeh Shafiee Roudbari, Zachary Patterson, Ursula Eicker, and Charalambos Poullis. TL;DR: We present a sequence-to-sequence architecture to extract the