4 papers
Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systems
Julien Boussard, Antoine Debouchage, Antoine Débouchage +1
A wide range of methods have been proposed, including physics-informed neural networks, which are powerful but do not guarantee identifiability of the dynamics, symbolic regression…
Unsupervised Causal Abstractions Discovery
Théo Saulus, Simon Lacoste-Julien, Dhanya Sridhar
Causal abstractions formalize when a high-level structural causal model (SCM) captures the interventional behavior of a lower-level SCM. Existing applications of this notion largel…
Who Guards the Guardians? The Challenges of Evaluating Identifiability of Learned Representations
Shruti Joshi, Théo Saulus, Wieland Brendel +3
Identifiability in representation learning is commonly evaluated using standard metrics (e.g., MCC, DCI, R^2) on synthetic benchmarks with known ground-truth factors. These metrics…
Improving Molecular Modeling with Geometric GNNs: an Empirical Study
Ali Ramlaoui, Théo Saulus, Basile Terver +4
Rapid advancements in machine learning (ML) are transforming materials science by significantly speeding up material property calculations. However, the proliferation of ML approac…