Nonparametric Bayesian Structure Adaptation for Continual Learning

Continual Learning is a learning paradigm where machine learning mode ls are trained with sequential or streaming tasks. Two notable directions among the recent adva nces in continual learning with neural networks are ( i) variational Bayes based regularization by learning priors from pre vious tasks, and, ( ii) learning the structure of deep networks to adapt to new tasks. S o far, these two approaches have been orthogonal. We present a principled nonparametric Bayesian appr oach for learning the structure of feed-forward neural networks, addressing the shortcomings o f both these approaches. In our model, the number of nodes in each hidden layer can automatically grow with the in troduction of each new task, and inter-task transfer occurs through the overlapping of differ ent sparse subsets of weights learned by different tasks. On benchmark datasets, our model performs comparably or better than the state-of-the-art approaches, while also being able to adaptively infer the evolving network structure in the continual learning setting.