Expectation Learning for Adaptive Crossmodal Stimuli Association

Pablo Barros , German I. Parisi , Di Fu , Xun Liu , Stefan Wermter

EUCog Meeting Proceedings, doi: 10.48550/arXiv.1801.07654 - Nov 2017 Open Access

Associated documents :

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.

@InProceedings{BPFLW17,
 	 author =  {Barros, Pablo and Parisi, German I. and Fu, Di and Liu, Xun and Wermter, Stefan},
 	 title = {Expectation Learning for Adaptive Crossmodal Stimuli Association},
 	 booktitle = {EUCog Meeting Proceedings},
 	 journal = {None},
 	 editors = {}
 	 number = {}
 	 volume = {}
 	 pages = {}
 	 year = {2017},
 	 month = {Nov},
 	 publisher = {EUCog Meeting},
 	 doi = {10.48550/arXiv.1801.07654},
 }