  Despite much research targeted at enabling conventional machine learning
models to continually learn tasks and data distributions sequentially without
forgetting the knowledge acquired, little effort has been devoted to account
for more realistic situations where learning some tasks accurately might be
more critical than forgetting previous ones. In this paper we propose a
Bayesian inference based framework to continually learn a set of real-world,
sensing-based analysis tasks that can be tuned to prioritize the remembering of
previously learned tasks or the learning of new ones. Our experiments prove the
robustness and reliability of the learned models to adapt to the changing
sensing environment, and show the suitability of using uncertainty of the
predictions to assess their reliability.
