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It is very common for neural networks to be asymmetric about the x axis, that is, to have many more nuerons in the first few layers than in the last few layers. Common example:

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But can neural networks be structured in such a way that they're asymmetric about the y axis, that is, such that some parts of the network are 'deeper' than others?

An example of where this could be useful is where a problem involves some unsophisticated features (that require only a small number of layers), and some much more sophisticated features (which may benefit greatly from having more layers - i.e. greater 'depth').

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"Wide and Deep network" was used in a 2016 paper.



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$\hspace{5cm}$Wide & Deep Learning for Recommender Systems


You may create using keras Concatenate layer

concat = keras.layers.Concatenate()([input, hidden2])

References to read -
The paper
Google blog
Another paper

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