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：神经网络训练的结果高度依赖于所选择的架构；即便仅对网络规模进行微小改动，通常也需要重新启动训练过程。与此相反，我们从小型架构开始训练，仅在问题需要时增加其容量，并在这一过程中避免干扰已有的优化。由此，我们引入一种基于自然梯度的方法，直观地在可能显著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，以及扩展分数的计算成本较低的下界。我们通过分类和回归问题（包括那些先验上适切架构规模存在显著不确定性的问题）展示了这种自扩展神经网络的优势。