16 citations · 18 across the 12 of their papers we have counts for
6 papers · 1 filter
SPLICE: Sparse Physics Library Identification and Compositional Expansion
Lucas Thil, Jesse Read, Guillaume Doquet +1
Neural operator splitting composes a library of neural operators, one per physical mechanism, into zero-shot simulators of new equations. Existing approaches, however, require thes…
Subspace-Decomposed JEPAs: Disentangling Progression and Content in Latent World Models
Lucas Thil, Jesse Read, Rim Kaddah +1
World models used for planning learn to predict how a scene evolves, yet they rarely hold a global view of how far a task has progressed. We argue that such a view should be part o…
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…
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…
Transferable Deep Metric Learning for Clustering
Simo Alami. C, Rim Kaddah, Jesse Read
Clustering in high dimension spaces is a difficult task; the usual distance metrics may no longer be appropriate under the curse of dimensionality. Indeed, the choice of the metric…
CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies
Simo Alami. C, Fernando Llorente, Rim Kaddah +2
Reinforcement Learning has drawn huge interest as a tool for solving optimal control problems. Solving a given problem (task or environment) involves converging towards an optimal…