9 citations · 20 across the 12 of their papers we have counts for
4 papers · 1 filter
Compete and Compose: Learning Independent Mechanisms for Modular World Models
Anson Lei, Frederik Nolte, Bernhard Schölkopf +1
We present COmpetitive Mechanisms for Efficient Transfer (COMET), a modular world model which leverages reusable, independent mechanisms across different environments. COMET is tra…
Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization
Haitz Saez de Ocariz Borde, Alvaro Arroyo, Ismael Morales +2
Recent research indicates that the performance of machine learning models can be improved by aligning the geometry of the latent space with the underlying data structure. Rather th…
Gromov-Hausdorff Distances for Comparing Product Manifolds of Model Spaces
Haitz Saez de Ocariz Borde, Alvaro Arroyo, Ismael Morales +2
Recent studies propose enhancing machine learning models by aligning the geometric characteristics of the latent space with the underlying data structure. Instead of relying solely…
Projections of Model Spaces for Latent Graph Inference
Haitz Sáez de Ocáriz Borde, Álvaro Arroyo, Ingmar Posner
Graph Neural Networks leverage the connectivity structure of graphs as an inductive bias. Latent graph inference focuses on learning an adequate graph structure to diffuse informat…