Depth-L ReLU networks with weight decay have native function spaces that are (2/L)-normable quasi-Banach spaces — the induced regularizer is not a norm for L > 2.
Deep networks are reproducing kernel chains.ArXiv, abs/2501.03697
2 Pith papers cite this work. Polarity classification is still indexing.
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Mirror flow reaches max-margin solutions in homogeneous neural networks where the mirror map choice controls whether learned features are sparse or dense while convergence can be exponentially slow.
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Representation Costs in Data Science: Foundations and the Quasi-Banach Spaces of Deep Neural Networks
Depth-L ReLU networks with weight decay have native function spaces that are (2/L)-normable quasi-Banach spaces — the induced regularizer is not a norm for L > 2.
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Implicit Bias of Mirror Flow in Homogeneous Neural Networks: Sparse and Dense Feature Learning
Mirror flow reaches max-margin solutions in homogeneous neural networks where the mirror map choice controls whether learned features are sparse or dense while convergence can be exponentially slow.