9 citations · 12 across the 3 of their papers we have counts for
5 papers
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification
Juan Maroñas, Daniel Hernández-Lobato
This work introduces the Efficient Transformed Gaussian Process (ETGP), a new way of creating C stochastic processes characterized by: 1) the C processes are non-stationary, 2) the…
Transforming Gaussian Processes With Normalizing Flows
Juan Maroñas, Oliver Hamelijnck, Jeremias Knoblauch +1
Gaussian Processes (GPs) can be used as flexible, non-parametric function priors. Inspired by the growing body of work on Normalizing Flows, we enlarge this class of priors through…
Solving Partial Differential Equations with Neural Networks
Juan B. Pedro, Juan Maroñas, Roberto Paredes
Many scientific and industrial applications require solving Partial Differential Equations (PDEs) to describe the physical phenomena of interest. Some examples can be found in the…
Bayesian Strategies for Likelihood Ratio Computation in Forensic Voice Comparison with Automatic Systems
Daniel Ramos, Juan Maroñas, Alicia Lozano-Diez
This paper explores several strategies for Forensic Voice Comparison (FVC), aimed at improving the performance of the LRs when using generative Gaussian score-to-LR models. First,…
Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks
Juan Maroñas, Roberto Paredes, Daniel Ramos
Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy performance in many tasks. However, recent works have pointed out that the outputs provided by these models are…