34 citations · 47 across the 6 of their papers we have counts for
3 papers · 1 filter
On Calibration of Mixup Training for Deep Neural Networks
Juan Maroñas, Daniel Ramos, Roberto Paredes
Deep Neural Networks (DNN) represent the state of the art in many tasks. However, due to their overparameterization, their generalization capabilities are in doubt and still a fiel…
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…
Generative Models For Deep Learning with Very Scarce Data
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. We compare the use of two well established techniques, Restricted Boltzm…