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In artificial neural networks, attention is a technique that is meant to mimic cognitive attention. This effect enhances some parts of the input data while diminishing other parts—the motivation being that the network should devote more focus to the important parts of the data, even though they may be a small portion of an image or sentence. Learning which part of the data is more important than another depends on the context, and this is trained by gradient descent.
Yixin Chen is a computer scientist, academic, and author. He is a professor of computer science and engineering at Washington University in St. Louis.
References
- ↑ Aggarwal, Charu C. "Neural Networks and Deep Learning" p240
- ↑ Leveraging Different Learning Rules in Hopfield Nets for Multiclass Classification saiconference.com
- ↑ 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. 15 April 2004.
- ↑ "Home". 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
- 1 2 3 4 5 https://scholar.google.com/scholar?hl=en&as_sdt=0%2C33&q=Amos+storkey&btnG= Google Scholar Author page, Accessed June 14, 2021
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