90 citations · 273 across the 18 of their papers we have counts for
18 papers
Deep learning from the crowd Fundamentals of morphological galaxy classification
Luis Enrique Sucar, Carlos del Burgo, Jonathan Serrano-Pérez
Aims. The objective of this work is to adapt a deep neural network model to perform galaxy morphological classification trained from crowd annotations, considering the training sch…
Bayesian and Convolutional Networks for Hierarchical Morphological Classification of Galaxies
Jonathan Serrano-Pérez, Raquel Díaz Hernández, L. Enrique Sucar
This work is focused on the morphological classification of galaxies following the Hubble sequence in which the different classes are arranged in a hierarchy. The proposed method,…
Semi-Supervised Hierarchical Multi-Label Classifier Based on Local Information
Jonathan Serrano-Pérez, L. Enrique Sucar
Scarcity of labeled data is a common problem in supervised classification, since hand-labeling can be time consuming, expensive or hard to label; on the other hand, large amounts o…
Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review
Sergio A. Serrano, Jose Martinez-Carranza, L. Enrique Sucar
Reinforcement Learning (RL) provides a framework in which agents can be trained, via trial and error, to solve complex decision-making problems. Learning with little supervision ca…
Similarity-based Knowledge Transfer for Cross-Domain Reinforcement Learning
Sergio A. Serrano, Jose Martinez-Carranza, L. Enrique Sucar
Transferring knowledge in cross-domain reinforcement learning is a challenging setting in which learning is accelerated by reusing knowledge from a task with different observation…
Knowledge-Based Hierarchical POMDPs for Task Planning
Sergio A. Serrano, Elizabeth Santiago, Jose Martinez-Carranza +2
The main goal in task planning is to build a sequence of actions that takes an agent from an initial state to a goal state. In robotics, this is particularly difficult because acti…