28 citations · 52 across the 7 of their papers we have counts for
8 papers
LSTM networks provide efficient cyanobacterial blooms forecasting even with incomplete spatio-temporal data
Claudia Fournier, Raul Fernandez-Fernandez, Samuel Cirés +3
Cyanobacteria are the most frequent dominant species of algal blooms in inland waters, threatening ecosystem function and water quality, especially when toxin-producing strains pre…
Neural Style Transfer with Twin-Delayed DDPG for Shared Control of Robotic Manipulators
Raul Fernandez-Fernandez, Marco Aggravi, Paolo Robuffo Giordano +2
Neural Style Transfer (NST) refers to a class of algorithms able to manipulate an element, most often images, to adopt the appearance or style of another one. Each element is defin…
Real Evaluations Tractability using Continuous Goal-Directed Actions in Smart City Applications
Raul Fernandez-Fernandez, Juan G. Victores, David Estevez +1
One of the most important challenges of Smart City Applications is to adapt the system to interact with non-expert users. Robot imitation frameworks aim to simplify and reduce time…
Neural Policy Style Transfer
Raul Fernandez-Fernandez, Juan G. Victores, Jennifer J. Gago +2
Style Transfer has been proposed in a number of fields: fine arts, natural language processing, and fixed trajectories. We scale this concept up to control policies within a Deep R…
Deep Robot Sketching: An application of Deep Q-Learning Networks for human-like sketching
Raul Fernandez-Fernandez, Juan G. Victores, Carlos Balaguer
The current success of Reinforcement Learning algorithms for its performance in complex environments has inspired many recent theoretical approaches to cognitive science. Artistic…
Transferring human emotions to robot motions using Neural Policy Style Transfer
Raul Fernandez-Fernandez, Bartek Łukawski, Juan G. Victores +1
Neural Style Transfer (NST) was originally proposed to use feature extraction capabilities of Neural Networks as a way to perform Style Transfer with images. Pre-trained image clas…