In this paper, we propose CSQN, a new Continual Learning (CL) method
which considers Quasi-Newton methods, more specifically, Sampled
Quasi-Newton methods, to extend EWC.
EWC uses a Bayesian framework to estimate which parameters are important
to previous tasks, and it punishes changes made to these parameters.
However, it assumes that parameters are independent, as it does not
consider interactions between parameters. With CSQN, we aim to overcome