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Found 11 papers, 5 papers with code
Transforming Gaussian Processes With Normalizing Flows
Gaussian Processes (GPs) can be used as flexible, non-parametric function priors.
On Calibration of Mixup Training for Deep Neural Networks
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.
Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models
The Gaussian process state-space model (GPSSM) has attracted extensive attention for modeling complex nonlinear dynamical systems.
Towards Flexibility and Interpretability of Gaussian Process State-Space Model
The Gaussian process state-space model (GPSSM) has garnered considerable attention over the past decade.
Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks
Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy performance in many tasks.
Generative Models For Deep Learning with Very Scarce Data
The goal of this paper is to deal with a data scarcity scenario where deep learning techniques use to fail.
Bayesian Strategies for Likelihood Ratio Computation in Forensic Voice Comparison with Automatic Systems
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.
Offline Deep models calibration with bayesian neural networks
We apply Bayesian Neural Networks to improve calibration of state-of-the-art deep neural networks.
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification
ETGPs exploit the recently proposed Transformed Gaussian Process (TGP), a stochastic process specified by transforming a Gaussian Process using an invertible transformation.
Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networks
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.
Deep Transformed Gaussian Processes
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.
