Andries Rosseau

Preserving Plasticity in Continual Learning via Dynamical Isometry

Continual training of deep neural networks under non-stationarity often leads to a progressive loss of plasticity, eventually limiting further learning. We define plasticity loss as the network’s diminishing ability to make reliable progress under gradient descent updates in all output-space directi…