Expectation Learning for Adaptive Crossmodal Stimuli Association

By Pablo V. A. Barros, German Ignacio Parisi, Di Fu, Xun Liu, Stefan Wermter. 2018

3 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Multimodal Learning, Statistical Learning Theory

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. German Ignacio Parisi
2. Christopher Kanan
3. Stefan Wermter
4. Tyler L. Hayes
5. Ronald Kemker
6. Vincenzo Lomonaco
7. Jose L. Part
8. Ali Ayub

In Experimental and Cognitive Psychology

1. German Ignacio Parisi
2. Stefan Wermter

In Multimodal Learning

1. Stefan Wermter
2. German Ignacio Parisi
3. Christopher Kanan
4. Kushal Kafle
5. Manoj Acharya
6. Robik Shrestha

In Statistical Learning Theory

1. Stefan Wermter
2. German Ignacio Parisi
3. Christopher Kanan

Citations in the corpus

• Continual Lifelong Learning with Neural Networks: A Review (2018, background): “Barros et al. (2017) proposed a deep architecture modelling crossmodal expectation learning.”