6 papers
Why Model Credibility Isn't Enough: -Rethinking Trust in Simulation Architectures
Romain Barbedienne, Adeline Lanugue, Rim Kaddah +6
Credibility of a simulation model is an important topic. Several approaches try to quantify the credibility of simulation. However, models are mostly assembled within a simulation…
Subspace-Decomposed JEPAs: Disentangling Progression and Content in Latent World Models
Lucas Thil, Jesse Read, Rim Kaddah +1
Joint-Embedding Predictive Architectures (JEPAs) learn compact latent world models by predicting future embeddings, but no single coordinate of the latent is designated to encode t…
STEP: Learning STructured Embeddings for Progressive Time Series
Lucas Thil, Jesse Read, Rim Kaddah +1
We present a novel method for learning interpretable representations of progressive time series, that is, data capturing irreversible state transitions such as degradation or task…
Parameter-Efficient Distributional RL via Normalizing Flows and a Geometry-Aware Cramér Surrogate
Simo Alami C., Rim Kaddah, Jesse Read +1
Distributional Reinforcement Learning (DistRL) improves upon expectation-based methods by modeling full return distributions, but standard approaches often remain far from parsimon…
I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation
Lucas Thil, Jesse Read, Rim Kaddah +1
Accurate remaining useful life (RUL) prediction hinges on the quality of health indicators (HIs), yet existing methods often fail to disentangle complex degradation mechanisms in m…
Uncertainty Quantification as a Complementary Latent Health Indicator for Remaining Useful Life Prediction on Turbofan Engines
Lucas Thil, Jesse Read, Rim Kaddah +1
Health Indicators (HIs) are essential for predicting system failures in predictive maintenance. While methods like RaPP (Reconstruction along Projected Pathways) improve traditiona…