Amos James Storkey
| Amos James Storkey | |
|---|---|
| Born | February 14, 1971 |
| 🏡 Residence | United Kingdom |
| 🏳️ Nationality | British |
| 🎓 Alma mater | Trinity College, Cambridge |
| 💼 Occupation | |
| Known for | Storkey Learning Rule First Convolutional Network for Learning Go |
Amos James Storkey is Professor of Machine Learning and Artificial Intelligence at the School of Informatics, University of Edinburgh, and a founding member of the European Laboratory for Learning and Intelligent Systems.[1]
Storkey studied mathematics at Trinity College, Cambridge and did doctoral work at Imperial College, London. In 1997 during his PhD, he worked on the Hopfield Network a form of recurrent artificial neural network popularized by John Hopfield in 1982. Hopfield nets serve as content-addressable ("associative") memory systems with binary threshold nodes and Storkey developed what became known as the "Storkey Learning Rule" .[2][3][4][5].
Subsequently, he has worked on approximate Bayesian methods, machine learning in astronomy[6], graphical models, inference and sampling, and neural networks. Storkey joined the School of Informatics at the University of Edinburgh in 1999, was Microsoft Research Fellow from 2003 to 2004, appointed as Reader in 2012, and to a personal Chair in 2018. He is currently a Member of Institute for Adaptive and Neural Computation, Director of CDT in Data Science [2014-22] leading the Bayeswatch Machine Learning Group[7]. In December 2014, Clark and Storkey together published an innovative paper "Teaching Deep Convolutional Neural Networks to Play Go". Convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. Their paper showed that a Convolutional Neural Network trained by supervised learning from a database of human professional games could outperform GNU Go and win some games against Monte Carlo tree search Fuego 1.1 in a fraction of the time it took Fuego to play.[8][9][10][11][circular reference]
References
- ↑ "European Laboratory for Learning and Intelligent Systems (ELLIS) launched with Informatics researchers on board".
- ↑ Aggarwal, Charu C. "Neural Networks and Deep Learning" p240
- ↑ https://saiconference.com/Downloads/FTC2017/Proceedings/62_Paper_426-Leveraging_Different_Learning_Rules_in_Hopfield.pdf
- ↑ Storkey, Amos. "Increasing the capacity of a Hopfield network without sacrificing functionality." Artificial Neural Networks – ICANN'97 (1997): 451-456.
- ↑ Storkey, Amos. "Efficient Covariance Matrix Methods for Bayesian Gaussian Processes and Hopfield Neural Networks". PhD Thesis. University of London. (1999)
- ↑ "One giant scrapheap for mankind". BBC News.
- ↑ https://www.bayeswatch.com/
- ↑ arXiv, Emerging Technology from the. "Why Neural Networks Look Set to Thrash the Best Human Go Players for the First Time". MIT Technology Review.
- ↑ Chris J Maddison, 'Move Evaluation in Go' Madhttp://www0.cs.ucl.ac.uk/staff/d.silver/web/Applications_files/deepgo.pdf
- ↑ Clark, Christopher; Storkey, Amos (2014). "Teaching Deep Convolutional Neural Networks to Play Go". arXiv:1412.3409 [cs.AI].
- ↑ Convolutional neural network
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