NGN: Learning Neural Network Size as a Differentiable Count

The paper introduces the Neurogenesis Network (NGN), a differentiable framework that learns the optimal number of ordered structural components in a neural network during training. By using a learnable boundary to select an active prefix of components, NGN can grow from a compact initialization and later discard unused parts. Experiments across MLPs, CNNs, GNNs, Transformers, state‑space models, LoRA, and adapters show that the learned prefixes perform comparably to fixed‑size models, demonstrating that structural capacity can be optimized directly as a count.

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• fine-tuning