Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning

Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash

Student First Author: No

Keywords: Online learning, Continual learning, Neuromorphic architectures, transfer metalearning, mixed-integer optimization

Abstract:

We focus on the problem of how to achieve online continual learning under
memory-constrained conditions where the input data may not be known \emph{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.