Name
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. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Crossmodal,Cognitive science,Unsupervised learning,Artificial intelligence,Deep learning,Stimulus (physiology),Machine learning,Mathematics |
DocType | Volume | Citations |
Journal | abs/1801.07654 | 1 |
PageRank | References | Authors |
0.39 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pablo V. A. Barros | 1 | 119 | 22.02 |
| German Ignacio Parisi | 2 | 248 | 21.75 |
| Di Fu | 3 | 8 | 5.00 |
| Xun Liu | 4 | 1 | 3.43 |
| Stefan Wermter | 5 | 32 | 6.00 |