The results of training a neural network are heavily dependent on the architecture chosen; and even a modification of only the size of the network, however small, typically involves restarting the training process. In contrast to this, we begin training with a small architecture, only increase its capacity as necessary for the problem, and avoid interfering with previous optimization while doing so. We thereby introduce a natural gradient based approach which intuitively expands both the width and depth of a neural network when this is likely to substantially reduce the hypothetical converged training loss. We prove an upper bound on the "rate" at which neurons are added, and a computationally cheap lower bound on the expansion score. We illustrate the benefits of such Self-Expanding Neural Networks in both classification and regression problems, including those where the appropriate architecture size is substantially uncertain a priori.

翻译：训练神经网络的結果严重依赖于所选的架构；即使仅对网络大小进行微小修改，通常也需要重新开始训练过程。与此相反，我们从一个小型架构开始训练，仅在问题需要时增加其容量，并在此过程中避免干扰先前的优化。由此，我们提出一种基于自然梯度的方法，该方法直觉上会在可能大幅降低假设收敛训练损失时扩展神经网络的宽度和深度。我们证明了神经元添加“速率”的上界，以及一个计算成本低廉的扩展分数下界。我们通过分类和回归问题（包括那些先验架构大小极不确定的问题）展示了这种自扩展神经网络的益处。