60 citations · 66 across the 5 of their papers we have counts for
5 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…
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…
Graph Neural Networks for Pressure Estimation in Water Distribution Systems
Huy Truong, Andrés Tello, Alexander Lazovik +1
Pressure and flow estimation in Water Distribution Networks (WDN) allows water management companies to optimize their control operations. For many years, mathematical simulation to…
Too Good To Be True: performance overestimation in (re)current practices for Human Activity Recognition
Andrés Tello, Victoria Degeler, Alexander Lazovik
Today, there are standard and well established procedures within the Human Activity Recognition (HAR) pipeline. However, some of these conventional approaches lead to accuracy over…