A new paper demonstrates that a wide range of feedforward neural network architectures possess finite sample complexity. This means they can learn effectively in the PAC model, even with unbounded parameters. The findings suggest that learnability is a baseline property for many modern architectures, shifting research focus to other aspects like inductive biases and optimization. AI
影响 Establishes finite sample complexity as a baseline for many neural network architectures, redirecting research focus to other architectural properties.
排序理由 The cluster contains an academic paper detailing theoretical findings about neural network learnability.
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