GradMax: Growing Neural Networks using Gradient Information

Understand

The architecture and the parameters of neural networks are often optimized independently, which requires costly retraining of the parameters whenever the architecture is modified.

• In this work we instead focus on growing the architecture without requiring costly retraining.
• We present a method that adds new neurons during training without impacting what is already learned, while improving the training dynamics.
• We achieve the latter by maximizing the gradients of the new weights and find the optimal initialization efficiently by means of the singular value decomposition (SVD).