283 citations · 802 across the 44 of their papers we have counts for
4 papers · 1 filter
Information-Based Calibration of Uncertainty Quantification in Product-of-Experts Gaussian Process Models
Yean Hoon Ong, Paolo Barucca, Wei Pan +1
Gaussian process (GP) regression with a single global GP (GP-glo) incurs cubic computational cost, limiting scalability to large datasets. Product-of-experts GP models (GP-pro), wh…
Characterizing Optimizer-Dependent Training Dynamics Through Hessian Eigenvector Displacement and Localization
Marcelina Marjankowska, Valerio Modugno, Paolo Barucca
Hessian spectral properties are a standard tool in analysing neural-network training, with eigenvalues linked to sharpness, generalization, and optimization dynamics. Eigenvalues q…
Fundamental Limits of Stability Inference in High-Dimensional Complex Systems
Michela Costa, Kentaro Hoshisashi, Flaviano Morone +2
Many complex systems, including ecosystems, neural circuits, and financial markets, are inferred to operate close to a threshold of instability, at which a small perturbation can p…
Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs
Kentaro Hoshisashi, Carolyn E Phelan, Paolo Barucca
Physics-Informed Neural Networks (PINNs) recast PDE solving as an optimisation problem in function space by minimising a residual-based objective, yet many applications require add…