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In papers on the robustness of neural networks, the concept of the ℓ norm ball is often used when generating perturbations. For example, ℓ norm balls involve ℓ1, ℓ2 and ℓ∞. May you recommend a...
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In other words, why have improvements to neural networks led to an increase in hyperparameters? Are hyperparameters related to some fundamental flaw of neural networks?
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