… novel continual learning framework including gradient decomposition, gradient optimization
and layerwise … We observe that optimizing the gradient update layerwise can further help the …

… a simple optimizer modification, which maintains the advantages of replay-based continual
learning while improving its stability. Our method, which we refer to as Layerwise Proximal Re…

… continual learning. However, by modifying the model only in the orthogonal direction to the
input space of old tasks, the optimization space of learning … We next define a layer-wise trust …

… learning scenario. This paper aims to apply complementary learning systems theory to improve
continual learning … The existing methods [22, 19] can suffer from suboptimal stability and …

… Such interference can be reduced with online meta-learning methods which optimize the
base learning algorithm using both present and past data, kept in a replay buffer [49, 15]. Our …

… a continual learning method referred to as Winning SubNetworks (WSN) which sequentially
learns and selects an optimal … continual learning, the model should consider the layer-wise …

… a layer-wise manner. GAN-Memory [167] takes advantage of FiLM [201] and AdaFM [202] to
… a wide basin in downstream continual learning by optimizing the flatness metric. SLCA [214] …

… different PLMs with a number of layer-wise probing analyses. … and optimization of better
PLM-oriented continual learning … the layer-wise insights of a specific PLM for continual learning. …

… Constant learning rate 0.001 is used for all layers and batch size B = 128. After each
epoch, we approximate the gradient variance factor Mk by computing the ratio of 1 …

… analysis, we next develop a ContinUal learning method with Backward … correlated old tasks
in a layer-wise manner, and then … To conclude, the optimization problem for learning the new …