6 papers
When, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregate
Florent Forest, Amaury Wei, Olga Fink
Time series extrinsic regression (TSER) refers to the task of predicting a continuous target variable from an input time series. It appears in many domains, including healthcare, f…
Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers
Chi-Ching Hsu, Gaëtan Frusque, Florent Forest +3
Commercial high-voltage circuit breaker (CB) condition monitoring systems rely on directly observable physical parameters such as gas filling pressure with pre-defined thresholds.…
Thermoxels: a voxel-based method to generate simulation-ready 3D thermal models
Etienne Chassaing, Florent Forest, Olga Fink +1
In the European Union, buildings account for 42% of energy use and 35% of greenhouse gas emissions. Since most existing buildings will still be in use by 2050, retrofitting is cruc…
Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis
Florent Forest, Olga Fink
Intelligent Fault Diagnosis (IFD) based on deep learning has proven to be an effective and flexible solution, attracting extensive research. Deep neural networks can learn rich rep…
Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in Regression
Ismail Nejjar, Gaetan Frusque, Florent Forest +1
Unsupervised Domain Adaptation for Regression (UDAR) aims to adapt models from a labeled source domain to an unlabeled target domain for regression tasks. Traditional feature align…
Exploiting Semantic Scene Reconstruction for Estimating Building Envelope Characteristics
Chenghao Xu, Malcolm Mielle, Antoine Laborde +3
Achieving the EU's climate neutrality goal requires retrofitting existing buildings to reduce energy use and emissions. A critical step in this process is the precise assessment of…