Expectation Learning for Adaptive Crossmodal Stimuli Association
Machine Learning
2018-01-24 v1 Artificial Intelligence
Sound
Neurons and Cognition
Machine Learning
Abstract
The human brain is able to learn, generalize, and predict crossmodal stimuli. Learning by expectation fine-tunes crossmodal processing at different levels, thus enhancing our power of generalization and adaptation in highly dynamic environments. In this paper, we propose a deep neural architecture trained by using expectation learning accounting for unsupervised learning tasks. Our learning model exhibits a self-adaptable behavior, setting the first steps towards the development of deep learning architectures for crossmodal stimuli association.
Cite
@article{arxiv.1801.07654,
title = {Expectation Learning for Adaptive Crossmodal Stimuli Association},
author = {Pablo Barros and German I. Parisi and Di Fu and Xun Liu and Stefan Wermter},
journal= {arXiv preprint arXiv:1801.07654},
year = {2018}
}
Comments
3 pages 2017 EUCog meeting abstract