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arXiv · 1801.07654

Expectation Learning for Adaptive Crossmodal Stimuli Association

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

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BibTeXRIS

Pablo Barros, German I. Parisi, Di Fu, Xun Liu, Stefan Wermter. 2018-01-23. Expectation Learning for Adaptive Crossmodal Stimuli Association. https://arxiv.org/abs/1801.07654

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