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Geoffrey Hinton

Geoffrey Hinton FRS (born December 6, 1947) is a British-born informatician most noted for his work on the mathematics and applications of neural networks, and their relationship to information theory. Hinton has contributed significantly to the scientific community in the fields of neural computation and cognitive science. His work in artificial intelligence has improved our understanding in how the human brain functions and more specifically, how it learns. Some of his contributions include the Boltzmann machine, backpropagation theory, distributed representations, the Helmholtz machine, and Product of Experts. Currently, his main interest lies in unsupervised learning of intelligent agents and neural networks. Geoff Hinton comes from a family rich in scientific and mathematical study. He is the great-great-grandson of logician and philosopher George Boole and the son of Howard E. Hinton, an entomologist.

Biography

Education

In this section I will outline Hinton's educational background.

Career

Here, I will describe Hinton's career so far and what his current research interests are.

Honours and Awards

This section will detail his honours and awards and any relevant information with regards to these awards.

Notable Contributions

In this section I will provide an outline of some of Hinton's most notable academic contributions to the scientific community.

References

1. Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. R. (2012) Improving neural networks by preventing co-adaptation of feature detectors http://arxiv.org/abs/1207.0580 2. G. Hinton, L. Deng, D. Yu, G. Dahl, A.Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kingsbury Deep Neural Networks for Acoustic Modeling in Speech Recognition IEEE Signal Processing Magazine, 29, November 2012 (in press) 3. Salakhutdinov, R. R. and Hinton, G. E. (2012) An Efficient Learning Procedure for Deep Boltzmann Machines. Neural Computation 4. Mohamed, A., Dahl, G. E. and Hinton, G. E. (2012) Acoustic Modeling using Deep Belief Networks. IEEE Trans. on Audio, Speech, and Language Processing, 20, pp 14-22. 5. Tang, Y., Salakhutdinov, R. R. and Hinton, G. E. (2012) Deep Lambertian Networks. International Conference on Machine Learning 6. Mnih, V. and Hinton, G. E. (2012) Learning to Label Aerial Images from Noisy Data. International Conference on Machine Learning 7. Tang, Y., Salakhutdinov, R. R. and Hinton, G. E. (2012) Deep Mixtures of Factor Analysers. International Conference on Machine Learning 8. Tang, Y., Salakhutdinov, R. R. and Hinton, G. E. (2012) Robust Boltzmann Machines for Recognition and Denoising. IEEE Conference on Computer Vision and Pattern Recognition 9. Mohamed,A., Hinton, G. E. and Penn, G. (2012) Understanding how Deep Belief Networks perform acoustic modelling ICASSP 2012, Kyoto 10. Suskever, I., Martens, J. and Hinton, G. E. (2011) Generating Text with Recurrent Neural Networks. Proc. 28th International Conference on Machine Learning, Seattle 11. Taylor, G. W, Hinton, G. E., and Roweis, S. (2011) Two distributed-state models for generating high-dimensional time series. Journal of Machine Learning Research, vol 12, pp 863-907 12. Ranzato, M., Susskind, J., Mnih, V. and Hinton, G. (2011) On deep generative models with applications to recognition. IEEE Conference on Computer Vision and Pattern Recognition 13. Mnih, V., Larochelle, H. and Hinton, G. (2011) Conditional Restricted Boltzmann Machines for Structured Output Prediction Uncertainty in Artificial Intelligence 14. Susskind,J., Memisevic, R., Hinton, G. and Pollefeys, M. (2011) Modeling the joint density of two images under a variety of transformations. IEEE Conference on Computer Vision and Pattern Recognition 15. Hinton, G. E., Krizhevsky, A. and Wang, S. (2011) Transforming Auto-encoders. ICANN-11: International Conference on Artificial Neural Networks, Helsinki

External Links

Here, I will provide links outside of Wikipedia which may be of use to those learning about Geoffrey Hinton or his psychological research.