Search Results for author: Juan Maroñas

Found 11 papers, 5 papers with code

Deep Transformed Gaussian Processes

no code implementations27 Oct 2023 Francisco Javier Sáez-Maldonado, Juan Maroñas, Daniel Hernández-Lobato

In this work, we propose a generalization of TGPs named Deep Transformed Gaussian Processes (DTGPs), which follows the trend of concatenating layers of stochastic processes.

Gaussian Processes Variational Inference

Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models

2 code implementations3 Sep 2023 Zhidi Lin, Juan Maroñas, Ying Li, Feng Yin, Sergios Theodoridis

The Gaussian process state-space model (GPSSM) has attracted extensive attention for modeling complex nonlinear dynamical systems.

Gaussian Processes Variational Inference

Towards Flexibility and Interpretability of Gaussian Process State-Space Model

1 code implementation21 Jan 2023 Zhid Lin, Feng Yin, Juan Maroñas

The Gaussian process state-space model (GPSSM) has garnered considerable attention over the past decade.

Variational Inference

Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networks

no code implementations31 Jul 2022 Sergio A. Balanya, Juan Maroñas, Daniel Ramos

We show that when there is plenty of data complex models like neural networks yield better performance, but are prone to fail when the amount of data is limited, a common situation in certain post-hoc calibration applications like medical diagnosis.

Inductive Bias Medical Diagnosis

Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification

no code implementations30 May 2022 Juan Maroñas, Daniel Hernández-Lobato

ETGPs exploit the recently proposed Transformed Gaussian Process (TGP), a stochastic process specified by transforming a Gaussian Process using an invertible transformation.

Gaussian Processes Multi-class Classification +1

On Calibration of Mixup Training for Deep Neural Networks

1 code implementation22 Mar 2020 Juan Maroñas, Daniel Ramos, Roberto Paredes

Data Augmentation (DA) strategies have been proposed to regularize these models, being Mixup one of the most popular due to its ability to improve the accuracy, the uncertainty quantification and the calibration of DNN.

Data Augmentation

Bayesian Strategies for Likelihood Ratio Computation in Forensic Voice Comparison with Automatic Systems

no code implementations18 Sep 2019 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.

Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks

1 code implementation23 Aug 2019 Juan Maroñas, Roberto Paredes, Daniel Ramos

Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy performance in many tasks.

Image Classification

Generative Models For Deep Learning with Very Scarce Data

no code implementations21 Mar 2019 Juan Maroñas, Roberto Paredes, Daniel Ramos

The goal of this paper is to deal with a data scarcity scenario where deep learning techniques use to fail.

General Classification

Offline Deep models calibration with bayesian neural networks

no code implementations27 Sep 2018 Juan Maroñas, Roberto Paredes, Daniel Ramos

We apply Bayesian Neural Networks to improve calibration of state-of-the-art deep neural networks.

Cannot find the paper you are looking for? You can Submit a new open access paper.