7 citations · 23 across the 5 of their papers we have counts for
6 papers · 1 filter
GenAutoML: An Agentic Framework for Dynamic Architecture Generation and Optimization in Time-Series Analysis
Oleeviya Babu Poikarayil, Cédric Schockaert, Abdulrahman Nahhas +3
Designing neural architectures for time-series forecasting and anomaly detection remains a resource-intensive task that often requires substantial domain expertise. Traditional Aut…
Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection
Shashank Mishra, Karan Patil, Cedric Schockaert +2
Root cause analysis (RCA) for time-series anomaly detection is critical for the reliable operation of complex real-world systems. Existing explanation methods often rely on unreali…
A Causal-based Framework for Multimodal Multivariate Time Series Validation Enhanced by Unsupervised Deep Learning as an Enabler for Industry 4.0
Cedric Schockaert
An advanced conceptual validation framework for multimodal multivariate time series defines a multi-level contextual anomaly detection ranging from an univariate context definition…
Attention Mechanism for Multivariate Time Series Recurrent Model Interpretability Applied to the Ironmaking Industry
Cedric Schockaert, Reinhard Leperlier, Assaad Moawad
Data-driven model interpretability is a requirement to gain the acceptance of process engineers to rely on the prediction of a data-driven model to regulate industrial processes in…
VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven Model Interpretability Applied to the Ironmaking Industry
Cedric Schockaert, Vadim Macher, Alexander Schmitz
Machine learning applied to generate data-driven models are lacking of transparency leading the process engineer to lose confidence in relying on the model predictions to optimize…
MTS-CycleGAN: An Adversarial-based Deep Mapping Learning Network for Multivariate Time Series Domain Adaptation Applied to the Ironmaking Industry
Cedric Schockaert, Henri Hoyez
In the current era, an increasing number of machine learning models is generated for the automation of industrial processes. To that end, machine learning models are trained using…