  Continual learning aims to enable machine learning models to learn a general
solution space for past and future tasks in a sequential manner. Conventional
models tend to forget the knowledge of previous tasks while learning a new
task, a phenomenon known as catastrophic forgetting. When using Bayesian models
in continual learning, knowledge from previous tasks can be retained in two
ways: 1). posterior distributions over the parameters, containing the knowledge
gained from inference in previous tasks, which then serve as the priors for the
following task; 2). coresets, containing knowledge of data distributions of
previous tasks. Here, we show that Bayesian continual learning can be
facilitated in terms of these two means through the use of natural gradients
and Stein gradients respectively.
