Abstract:
Deep learning’s success makes understanding its mechanism and rationality urgent. Deep learning requires data depth, structure, and volume. Most deep learning theoretical studies emphasize the importance and benefits of neural network depth and structure. This article rigorously verifies massive data’s role in deep learning’s outperformance. In particular, we show that massive data is needed to realize spatial sparseness, and deep nets are essential tools for this application. These findings explain why deep learning succeeds in big data despite deep nets and other network structures being proposed at least 20 years ago.
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