Learning multiple layers of representation

@article{Hinton2007LearningML,
  title={Learning multiple layers of representation},
  author={Geoffrey E. Hinton},
  journal={Trends in Cognitive Sciences},
  year={2007},
  volume={11},
  pages={428-434}
}
To achieve its impressive performance in tasks such as speech perception or object recognition, the brain extracts multiple levels of representation from the sensory input. Backpropagation was the first computationally efficient model of how neural networks could learn multiple layers of representation, but it required labeled training data and it did not work well in deep networks. The limitations of backpropagation learning can now be overcome by using multilayer neural networks that contain… Expand
Learning Representations from Deep Networks Using Mode Synthesizers
Autonomous Learning of Representations
Modeling language and cognition with deep unsupervised learning: a tutorial overview
Contributions to Deep Learning Models
Learning Deep Visual Representations
Deep learning models of biological visual information processing
A review on advances in deep learning
  • Soniya, Sandeep Paul, Lotika Singh
  • Computer Science
  • 2015 IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI)
  • 2015
Vector LIDA
Deep Learning in Kernel Machines
Interpreting faces with neurally inspired generative models
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 60 REFERENCES
A Fast Learning Algorithm for Deep Belief Nets
Unsupervised Learning of Image Transformations
...
1
2
3
4
5
...