Neurogenesis in ANNs is an understudied and difficult problem, even compared to other forms of structural learning like pruning. By decomposing it into triggers and initializations, we introduce a framework for studying the various facets of neurogenesis: when, where, and how to add neurons during the learning process. We present the Neural Orthogonality (NORTH*) suite of neurogenesis strategies, combining layer-wise triggers and initializations based on the orthogonality of activations or weights to dynamically grow performant networks that converge to an efficient size. We evaluate our contributions against other recent neurogenesis works with MLPs.

翻译：ANNs 神经发源是一个研究不足和困难的问题,即使与诸如裁剪等其他结构学习形式相比也是如此。通过将其分解为触发和初始化,我们引入了一个框架来研究神经发源的各个方面:在学习过程中,何时、何地以及如何添加神经元。我们展示了神经异端神经发源战略套件,结合了基于电动或重量的交替式触发和初始化,以形成快速增长的功能性网络,这些网络聚集在高效的大小上。我们评估了我们相对于其他近期神经发源与MLPs合作而做出的贡献。