Author

A. Masegosa

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#machine learning Preprint Oct 2026

Contrastive Linearized Laplace: Loss Geometry, Valid Repairs, and Reliable Cores

While contrastive learning is widely used to learn deterministic representations, it does not inherently provide estimates of uncertainty. Post-hoc uncertainty estimation methods, such as the Linearized Laplace approximation (LLA), offer a way to quantify uncertainty without retraining the encoder. However, we show tha...

F. J. Sáez-Maldonado, Luis A. Ortega, Andrés R. Masegosa et al. · 0 citations

#machine learning Preprint Oct 2026

Generalized Matheron Variational Implicit Processes

Implicit-process priors specify distributions over functions through sample-forward mechanisms such as Bayesian neural networks and stochastic simulators, but their function-space densities are typically unavailable. We introduce Generalized Matheron Variational Implicit Processes (GMVIP), a pathwise variational family...

Luis A. Ortega, Andrés R. Masegosa, Thomas D. Nielsen · 0 citations

#machine learning Preprint Sep 2026

Sequence-Informed Geometric Evaluation of RNA 3D Structures

Computational RNA structure pipelines generate many candidate conformations for the same sequence. Reliable evaluation therefore requires more than recognising plausible geometry, it requires determining whether that geometry is compatible with the sequence. We introduce SIRGE, a sequence-informed geometric evaluator t...

Andrea Zerio, Yi-Song Yao, Alessandro Micheli et al. · 0 citations