activity
20192022
most citedOn Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach

18 citations · 29 across the 9 of their papers we have counts for

collaborators

11 papers

cs.CY20221 cited

The potential of artificial intelligence for achieving healthy and sustainable societies

B. Sirmacek, S. Gupta, F. Mallor +10

In this chapter we extend earlier work (Vinuesa et al., Nature Communications 11, 2020) on the potential of artificial intelligence (AI) to achieve the 17 Sustainable Development G…

cs.LG20211 cited

Interpretable deep-learning models to help achieve the Sustainable Development Goals

Ricardo Vinuesa, Beril Sirmacek

We discuss our insights into interpretable artificial-intelligence (AI) models, and how they are essential in the context of developing ethical AI systems, as well as data-driven s…

astro-ph.EP2021

Environmental thresholds for mass-extinction events

Guy R. McPherson, Beril Sirmacek, Ricardo Vinuesa

While the global-average temperatures are rapidly rising, more researchers have been shifting their focus towards the past mass-extinction events in order to show the relations bet…

eess.IV20214 cited

Recurrent U-net for automatic pelvic floor muscle segmentation on 3D ultrasound

Frieda van den Noort, Beril Sirmacek, Cornelis H. Slump

The prevalance of pelvic floor problems is high within the female population. Transperineal ultrasound (TPUS) is the main imaging modality used to investigate these problems. Autom…

cs.LG20211 cited

Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics

Nicolò Botteghi, Mannes Poel, Beril Sirmacek +1

Deep Reinforcement Learning has shown its ability in solving complicated problems directly from high-dimensional observations. However, in end-to-end settings, Reinforcement Learni…

physics.flu-dyn2021

From coarse wall measurements to turbulent velocity fields through deep learning

Alejandro Güemes, Stefano Discetti, Andrea Ianiro +3

This work evaluates the applicability of super-resolution generative adversarial networks (SRGANs) as a methodology for the reconstruction of turbulent-flow quantities from coarse…