arXiv:2607. 23397v1 Announce Type: new Abstract: Hierarchical neural networks are widely used in artificial intelligence, yet their mathematical properties remain incompletely understood.
By Sumio Watanabe
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.
By Lixing Li
Over the past decade, deep neural networks (DNNs) have achieved remarkable success on complex machine-learning tasks, yet the theoretical foundations of their performance remain incomplete. From a statistical viewpoint, a natural question is: can DNNs attain feature-learning and prediction consistency comparable to that of classical models?
arXiv:2605. 15435v2 Announce Type: replace Abstract: Standard deep-learning pipelines usually choose the network architecture before training and keep it fixed throughout optimization.
By Lute Lillo, Nick Cheney
arXiv:2607. 03148v1 Announce Type: cross Abstract: Activation functions are considered an essential primitive for neural nonlinearity, i.
By Muhammad Sabih, Frank Hannig, J\"urgen Teich
arXiv:2607. 09967v1 Announce Type: cross Abstract: Many neural networks operations have a multiplicative nature rather than additive: halving or doubling a norm are analogous relatively but require unequal optimization distances when taking linear steps.
By Ethan Smith