  Continual learning (CL) aims to train deep neural networks efficiently on
streaming data while limiting the forgetting caused by new tasks. However,
learning transferable knowledge with less interference between tasks is
difficult, and real-world deployment of CL models is limited by their inability
to measure predictive uncertainties. To address these issues, we propose
handling CL tasks with neural processes (NPs), a class of meta-learners that
encode different tasks into probabilistic distributions over functions all
while providing reliable uncertainty estimates. Specifically, we propose an
NP-based CL approach (NPCL) with task-specific modules arranged in a
hierarchical latent variable model. We tailor regularizers on the learned
latent distributions to alleviate forgetting. The uncertainty estimation
capabilities of the NPCL can also be used to handle the task head/module
inference challenge in CL. Our experiments show that the NPCL outperforms
previous CL approaches. We validate the effectiveness of uncertainty estimation
in the NPCL for identifying novel data and evaluating instance-level model
confidence. Code is available at \url{https://github.com/srvCodes/NPCL}.
