4 papers
T3R: Deeper Test-Time Adaptation for Graph Neural Networks via Gradient Rotation
Huy Truong, Alexander Lazovik, Victoria Degeler
Graph Neural Networks (GNNs) deployed in real-world systems typically have fixed weights, often leading to degraded performance under distribution shifts. This issue can be mitigat…
A Multivariate Statistical Framework for Detection, Classification and Pre-localization of Anomalies in Water Distribution Networks
Oleg Melnikov, Yurii Dorofieiev, Yurii Shakhnovskiy +2
This paper presents a unified framework, for the detection, classification, and preliminary localization of anomalies in water distribution networks using multivariate statistical…
DiTEC-WDN: A Large-Scale Dataset of Hydraulic Scenarios across Multiple Water Distribution Networks
Huy Truong, Andrés Tello, Alexander Lazovik +1
Privacy restrictions hinder the sharing of real-world Water Distribution Network (WDN) models, limiting the application of emerging data-driven machine learning, which typically re…
Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks
Andres Tello, Huy Truong, Alexander Lazovik +1
Currently, the number of common benchmark datasets that researchers can use straight away for assessing data-driven deep learning approaches is very limited. Most studies provide d…