Deep learning
Citations
1,026 citations
Cites background from "Deep learning"
...…it is true that the representations learned by many state-of-the-art machine learning approaches— most notably “deep” artificial neural networks (LeCun et al., 2015)—can be impenetrable to human comprehension (but see Zeiler & Fergus, 2014), this is a property of specific approaches or…...
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...…virtually all other models—as is increasingly the case in many psychologyrelated domains such as computer vision and natural language processing (LeCun et al., 2015)—one may want to consider the possibility that simpler, more interpretable models are simply not adequate for explaining the…...
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1,010 citations
995 citations
994 citations
Cites background from "Deep learning"
...l., 2007), and land-use classification (Pacifici et al., 2009). Here, we briefly review the structure of neural networks. For more details and the latest advances, readers can refer to (Bishop, 1995; LeCun et al., 2015). Neural networks are made up of a large number of simple processing units called nodes or neurons. The main task of a neuron is to receive input from its neighbors, to compute an output and to send t...
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984 citations
Cites methods from "Deep learning"
...Deep and neural learning methods are now well established in machine learning (LeCun et al., 2015; Bengio, 2009)....
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References
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