Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning

We focus on the problem of how to achieve online continual learning under memory-constrained conditions where the input data may not be known a priori. These constraints are relevant in edge computing scenarios. We have developed an architecture where input processing over data streams and online learning are integrated in a single recurrent network architecture. This allows us to cast metalearning optimization as a mixed-integer optimization problem, where different synaptic plasticity algorithms and feature extraction layers can be swapped out and their hyperparameters are optimized to identify optimal architectures for different sets of tasks. We utilize a Bayesian optimization method to search over a design space that spans multiple learning algorithms, their specific hyperparameters, and feature extraction layers. We demonstrate our approach for online non-incremental and class-incremental learning tasks. Our optimization algorithm finds configurations that achieve superior continual learning performance on Split-MNIST and Permuted-MNIST data as compared with other memory-constrained learning approaches, and it matches that of the state-of-the-art memory replay-based approaches without explicit data storage and replay. Our approach allows us to explore the transferability of optimal learning conditions to tasks and datasets that have not been previously seen. We demonstrate that the accuracy of our transfer metalearning across datasets can be largely explained through a transfer coefficient that can be based on metrics of dimensionality and distance between datasets.