5 papers · 1 filter
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…
A Machine Learning Framework for Turbofan Health Estimation via Inverse Problem Formulation
Milad Leyli-Abadi, Lucas Thil, Sebastien Razakarivony +2
Estimating the health state of turbofan engines is a challenging ill-posed inverse problem, hindered by sparse sensing and complex nonlinear thermodynamics. Research in this area r…
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…
Navigating WebAI: Training Agents to Complete Web Tasks with Large Language Models and Reinforcement Learning
Lucas-Andreï Thil, Mirela Popa, Gerasimos Spanakis
Recent advancements in language models have demonstrated remarkable improvements in various natural language processing (NLP) tasks such as web navigation. Supervised learning (SL)…