34 citations · 47 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Adversarial Ladder Networks
Juan Maroñas Molano, Alberto Albiol Colomer, Roberto Paredes Palacios
The use of unsupervised data in addition to supervised data in training discriminative neural networks has improved the performance of this clas- sification scheme. However, the be…
LAYERS: Yet another Neural Network toolkit
Roberto Paredes, José-Miguel Benedí
Layers is an open source neural network toolkit aim at providing an easy way to implement modern neural networks. The main user target are students and to this end layers provides…