In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning
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We present experiments demonstrating that some other form of capacity control, different from network size, plays a central role in learning multilayer feed-forward networks. We argue, partially through analogy to matrix factorization, that this is an inductive bias that can help shed light on deep learning.
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