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[R] Gap between the actual and theoretical neural net capacity?

Intuitively, larger networks have higher capacity than smaller ones. However, the theoretical capacity of a huge network would never be reached in practice due to inefficient optimization procedure, limited dataset etc. So if we scale a network by 10 times, its actual capacity might only increase by eg. 5 times, and if we scale it by 100 times, the actual capacity could increase by only 20 times.

Is such a claim correct? Are there any papers that study the gap between the actual and theoretical network gap or relevant topic?

submitted by /u/vernunftig
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.