112 citations · 180 across the 13 of their papers we have counts for
13 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…
Tabular Foundation Models Can Learn Association Rules
Erkan Karabulut, Daniel Daza, Paul Groth +2
Association Rule Mining (ARM) is a fundamental task for knowledge discovery in tabular data and is widely used in high-stakes decision-making. Classical ARM methods rely on frequen…
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…
Discovering Association Rules in High-Dimensional Small Tabular Data
Erkan Karabulut, Daniel Daza, Paul Groth +1
Association Rule Mining (ARM) aims to discover patterns between features in datasets in the form of propositional rules, supporting both knowledge discovery and interpretable machi…
Neurosymbolic Association Rule Mining from Tabular Data
Erkan Karabulut, Paul Groth, Victoria Degeler
Association Rule Mining (ARM) is the task of mining patterns among data features in the form of logical rules, with applications across a myriad of domains. However, high-dimension…
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…
Learning Semantic Association Rules from Internet of Things Data
Erkan Karabulut, Paul Groth, Victoria Degeler
Association Rule Mining (ARM) is the task of discovering commonalities in data in the form of logical implications. ARM is used in the Internet of Things (IoT) for different tasks…
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…
AE SemRL: Learning Semantic Association Rules with Autoencoders
Erkan Karabulut, Victoria Degeler, Paul Groth
Association Rule Mining (ARM) is the task of learning associations among data features in the form of logical rules. Mining association rules from high-dimensional numerical data,…
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…
Semantic Association Rule Learning from Time Series Data and Knowledge Graphs
Erkan Karabulut, Victoria Degeler, Paul Groth
Digital Twins (DT) are a promising concept in cyber-physical systems research due to their advanced features including monitoring and automated reasoning. Semantic technologies suc…
Ontologies in Digital Twins: A Systematic Literature Review
Erkan Karabulut, Salvatore F. Pileggi, Paul Groth +1
Digital Twins (DT) facilitate monitoring and reasoning processes in cyber-physical systems. They have progressively gained popularity over the past years because of intense researc…