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One of the most fundamental design choices in neural networks is layer width: it affects the capacity of what a network can learn and determines the complexity of the solution.
Adaptive compression-based lifelong learning
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Earlier work this paper cites.
Learning Internal Representations by Error Propagation , pp. 318–362
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1986
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