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This research introduces Neurogenesis Networks (NGN), a method that treats neural network size as a differentiable variable to be optimized directly during training rather than fixed beforehand. By learning the optimal number of structural components for various architectures like Transformers and MLPs, the study demonstrates that this dynamic approach yields performance comparable to standard fixed-size models while enabling efficient deployment through prefix selection.
NGN: Learning Neural Network Size as a Differentiable Count
arXiv:2609.27291v1 Announce Type: new Abstract: Neural network size is usually chosen before training, separating architecture selection from weight optimization. We introduce the Neurogenesis Network (NGN), a differentiable parameterization for learning how many ordered structural components a model should use. For each ordered component group, one learnable boundary selects an active prefix while the model parameters are trained. The boundary can grow from a compact initialization and can be deployed by discarding components beyond the learned boundary. Controlled experiments examine convergence of the learned boundary, the performance of deployed prefixes, and comparisons with fixed-size models and alternative approaches to learning capacity. We then apply the same mechanism to MLPs, convolutional and graph networks, Transformers, state-space models, LoRA, and adapters. Across these settings, deploying only the learned prefix usually changes performance little, and the selected architectures perform similarly to fixed models trained at the same size. These results show that structural capacity can be optimized directly as a count.
Abstract:Neural network size is usually chosen before training, separating architecture selection from weight optimization. We introduce the Neurogenesis Network (NGN), a differentiable parameterization for learning how many ordered structural components a model should use. For each ordered component group, one learnable boundary selects an active prefix while the model parameters are trained. The boundary can grow from a compact initialization and can be deployed by discarding components beyond the learned boundary. Controlled experiments examine convergence of the learned boundary, the performance of deployed prefixes, and comparisons with fixed-size models and alternative approaches to learning capacity. We then apply the same mechanism to MLPs, convolutional and graph networks, Transformers, state-space models, LoRA, and adapters. Across these settings, deploying only the learned prefix usually changes performance little, and the selected architectures perform similarly to fixed models trained at the same size. These results show that structural capacity can be optimized directly as a count.
Machine Learning (cs.LG)
arXiv:2609.27291 [cs.LG]
(or arXiv:2609.27291v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.27291 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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