53 citations · 53 across the 1 of their papers we have counts for
2 papers
cs.NE2022★ 53 cited
EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning based Deep Neural Networks
Javier Poyatos, Daniel Molina, Aritz. D. Martinez +2
In recent years, Deep Learning models have shown a great performance in complex optimization problems. They generally require large training datasets, which is a limitation in most…
cs.RO2019
Learn and Link: Learning Critical Regions for Efficient Planning
Daniel Molina, Kislay Kumar, Siddharth Srivastava
This paper presents a new approach to learning for motion planning (MP) where critical regions of an environment are learned from a given set of motion plans and used to improve pe…