48 citations · 89 across the 4 of their papers we have counts for
Showing 2019Show all
2 papers · 1 filter
cs.LG2019★ 34 cited
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…
cs.LG2019★ 7 cited
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…